{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Ukraine War\Ukraine Jan 2023\C&C\CC Revision 1\CC Revision 2\Final Version\CC Replication Instructions\CC Ukraine July 2022 replication log file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}27 Jan 2025, 16:52:51

{com}. do "C:\Users\swhitt\OneDrive - High Point University\Research\Ukraine War\Ukraine Jan 2023\C&C\CC Revision 1\CC Revision 2\Final Version\CC Replication Instructions\CC Ukraine July 2022 replication do file.do"
{txt}
{com}. *Replication Instructions for
. 
. *War-Related Victimization and Social Distance Toward Others: Evidence Following Russia's 2022 Invasion of Ukraine
. 
. *Sam Whitt, Douglas Page
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA. There are multiple replication datasets and do files, which are noted for each table and figure. Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication. 
. 
. *Note: You may need to install STATA packages for the cibar, catcibar, and iebaltab commands. Use findit with the command name to identify and download the appropriate packets to install. 
. 
. *Note: In addition, some graphs require additional formatting using filename.grec files with the graph play command. To format a graph, simply run the command to generate the graph in the do file in STATA, then open the "Graph Editor" in STATA and click on the GREEN "Play Recording" button, then select "Browse" to select the grec file from the folder "grec files for STATA graph formatting" among Replication files. The name of the grec file is indicated in the note below the graph command in the do file for the specific graph you wish to format. This should automatically format the graph, which you may then save to a location of your choosing.
. 
. *Manuscript Replication
. 
. *"Stata user generated commands to install for replication purposes"
. 
. *"cibar"
. 
. ssc install cibar, replace
{txt}checking {hilite:cibar} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"iebaltab from ietoolkit"
. 
. ssc install ietoolkit, replace
{txt}checking {hilite:ietoolkit} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"catcibar"
. 
. net install catcibar, from("https://aarondwolf.github.io/catcibar") replace
checking {hilite:catcibar} consistency and verifying not already installed...
all files already exist and are up to date.
{txt}
{com}. 
. 
. *In Text Replication up to robustness checks
. 
. *Use "CC Ukraine July 2022 replication data.dta"
. 
. *In terms of direct experiences with victimization, less than 1% report having been injured or knowing someone who was sexually assaulted, but 34% report having close friends or family injured or killed since February 24th, almost exclusively by Russian (91%) or unknown forces (7%).
. 
. tab injured 

               {txt}S1.a). Were you wounded? {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,964       98.20       98.20
{txt}                 Yes, by Russian Forces {c |}{res}         14        0.70       98.90
{txt}              Yes, by Ukrainian  Forces {c |}{res}          1        0.05       98.95
{txt}�Yes, by both Russian and Ukrainian for {c |}{res}          3        0.15       99.10
{txt}Yes, by another group or unsure by whom {c |}{res}          9        0.45       99.55
{txt}                            HARD TO SAY {c |}{res}          4        0.20       99.75
{txt}                      REFUSAL TO ANSWER {c |}{res}          5        0.25      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. tab sexassault  

  {txt}S1.c). Were any of your close friends {c |}
   or family members sexually abused or {c |}
                                  assau {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,951       97.55       97.55
{txt}                 Yes, by Russian Forces {c |}{res}         22        1.10       98.65
{txt}              Yes, by Ukrainian  Forces {c |}{res}          3        0.15       98.80
{txt}Yes, by another group or unsure by whom {c |}{res}          2        0.10       98.90
{txt}                            HARD TO SAY {c |}{res}         18        0.90       99.80
{txt}                      REFUSAL TO ANSWER {c |}{res}          4        0.20      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. tab faminjured 

  {txt}S1.b). Were any of your close friends {c |}
   or family members wounded or killed? {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,313       65.65       65.65
{txt}                 Yes, by Russian Forces {c |}{res}        596       29.80       95.45
{txt}              Yes, by Ukrainian  Forces {c |}{res}          4        0.20       95.65
{txt}�Yes, by both Russian and Ukrainian for {c |}{res}         12        0.60       96.25
{txt}Yes, by another group or unsure by whom {c |}{res}         45        2.25       98.50
{txt}                            HARD TO SAY {c |}{res}         25        1.25       99.75
{txt}                      REFUSAL TO ANSWER {c |}{res}          5        0.25      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. 
. *note: 91% comes from 596 out of 657 who indicate family or friends injured or killed by Russian forces. 7% comes from 45 out of 657 who indicated family or friends injured/killed by unknown forces.
. 
. *Nearly 12% report that their home or property was damaged or destroyed since the war started, while 14% report being internally displaced, with 4% displaced from newly occupied territories. 
. 
. tab homedestroyed

       {txt}S1 d). Was your home or property {c |}
                  damaged or destroyed? {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,747       87.35       87.35
{txt}                 Yes, by Russian Forces {c |}{res}        182        9.10       96.45
{txt}              Yes, by Ukrainian  Forces {c |}{res}          3        0.15       96.60
{txt}�Yes, by both Russian and Ukrainian for {c |}{res}          9        0.45       97.05
{txt}Yes, by another group or unsure by whom {c |}{res}         39        1.95       99.00
{txt}                            HARD TO SAY {c |}{res}         17        0.85       99.85
{txt}                      REFUSAL TO ANSWER {c |}{res}          3        0.15      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. 
. *note: 12% comes from 233 out of 2000 who report property destruction. 
. 
. tab moved

  {txt}dummy for {c |}
moved since {c |}
   Feb 24th {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,725       86.25       86.25
{txt}          1 {c |}{res}        275       13.75      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,000      100.00
{txt}
{com}. tab occupied 

{txt}dummy for 1 {c |}
     = from {c |}
   occupied {c |}
  territory {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,926       96.30       96.30
{txt}          1 {c |}{res}         74        3.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,000      100.00
{txt}
{com}. 
. *In our survey, 94% of the sample identifies as Ukrainian by `nationality', though only 73% generally prefer speaking in Ukrainian while 20% favor Russian.
.  
. tab nationality

      {txt}D4. With what nationality do you {c |}
                    associate yourself {c |}      Freq.     Percent        Cum.
{hline 39}{c +}{hline 35}
                             Ukrainian {c |}{res}      1,888       94.40       94.40
{txt}                               Russian {c |}{res}         49        2.45       96.85
{txt}Ukrainian and Russian (only voluntary) {c |}{res}         19        0.95       97.80
{txt}                               Belarus {c |}{res}          1        0.05       97.85
{txt}                             Moldavian {c |}{res}          4        0.20       98.05
{txt}                         Crimean Tatar {c |}{res}          1        0.05       98.10
{txt}                             Bulgarian {c |}{res}          4        0.20       98.30
{txt}                             Hungarian {c |}{res}          1        0.05       98.35
{txt}                                  Pole {c |}{res}          4        0.20       98.55
{txt}                                   Jew {c |}{res}          4        0.20       98.75
{txt}                                 Other {c |}{res}         14        0.70       99.45
{txt}                           HARD TO SAY {c |}{res}         11        0.55      100.00
{txt}{hline 39}{c +}{hline 35}
                                 Total {c |}{res}      2,000      100.00
{txt}
{com}. tab language

   {txt}What language is more comfortable to {c |}
           respondent in talking to you {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                              Ukrainian {c |}{res}      1,459       72.95       72.95
{txt}                                Russian {c |}{res}        393       19.65       92.60
{txt}Equally, but more often talks in Ukrain {c |}{res}         65        3.25       95.85
{txt}      Hard to say - answer in Ukrainian {c |}{res}         18        0.90       96.75
{txt}Equally, but more often talks in Russia {c |}{res}         44        2.20       98.95
{txt}        Hard to say - answer in Russian {c |}{res}         21        1.05      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. 
. *Finally, our occupation variable includes limited active-duty military personnel (2.3%) because they are currently deployed and not participating in telephone surveys. 
. 
. tab occupation

                        {txt}D6. Occupation {c |}      Freq.     Percent        Cum.
{hline 39}{c +}{hline 35}
                    Worker, farmworker {c |}{res}        317       15.91       15.91
{txt}    Servant (without higher education) {c |}{res}        169        8.48       24.40
{txt}  Professional (with higher education) {c |}{res}        416       20.88       45.28
{txt}       Self employed businesswomen/men {c |}{res}        103        5.17       50.45
{txt}                  Entrepreneur, farmer {c |}{res}        106        5.32       55.77
{txt}                      Military servant {c |}{res}         45        2.26       58.03
{txt}                           Householder {c |}{res}        138        6.93       64.96
{txt}Pension (because of age or disability) {c |}{res}        485       24.35       89.31
{txt}                               Student {c |}{res}         63        3.16       92.47
{txt}                            Unemployed {c |}{res}        150        7.53      100.00
{txt}{hline 39}{c +}{hline 35}
                                 Total {c |}{res}      1,992      100.00
{txt}
{com}. 
. *Average feelings of closeness are much higher for ethnic Ukrainians than ethnic Russians (paired t-test = 55.3, p<0.0000) and even greater for citizens of Ukraine compared to citizens of Russia (paired t-test  = 133.7, p<0.0000). 
. 
. ttest ethnicuk = ethnicru

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
ethnicuk {c |}{res}{col 12}  1,832{col 22} 8.812227{col 34} .0508321{col 46} 2.175707{col 58} 8.712532{col 70} 8.911922
{txt}ethnicru {c |}{res}{col 12}  1,832{col 22} 3.808952{col 34} .0826414{col 46} 3.537204{col 58} 3.646871{col 70} 3.971033
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}  1,832{col 22} 5.003275{col 34} .0905439{col 46} 3.875449{col 58} 4.825695{col 70} 5.180855
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}ethnicuk{txt} - {res}ethnicru{txt})                       t = {res} 55.2580
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}    1831

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest citizenuk = citizenru

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
citize~k {c |}{res}{col 12}  1,924{col 22}  9.52183{col 34} .0293175{col 46} 1.285966{col 58} 9.464332{col 70} 9.579327
{txt}citize~u {c |}{res}{col 12}  1,924{col 22} 1.189709{col 34} .0531448{col 46} 2.331114{col 58} 1.085481{col 70} 1.293936
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}  1,924{col 22} 8.332121{col 34} .0623403{col 46} 2.734458{col 58} 8.209859{col 70} 8.454382
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}citizenuk{txt} - {res}citizenru{txt})                     t = {res}133.6555
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}    1923

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *On average, respondents rate ethnic Ukrainians five points higher on closeness than ethnic Russians within Ukraine, and 79% feel closer to ethnic Ukrainians than ethnic Russians. 
. 
. sum ethnicuk

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}ethnicuk {c |}{res}      1,922    8.806452    2.186514          0         10
{txt}
{com}. sum ethnicru

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}ethnicru {c |}{res}      1,879    3.783928    3.541499          0         10
{txt}
{com}. 
. *Code for generating ethnic bias dependent variable (already generated)
. *gen magukbias = ethnicuk-ethnicru
. *gen dukbias = 1 if magukbias>0
. *replace dukbias = 0 if magukbias<1
. *replace dukbias = . if magukbias==.
. *tab dukbias
. 
. *In terms of social distance across citizenship, Ukrainian citizens are placed 8 points higher on closeness than citizens of Russia, and 96% feel closer to Ukrainian than Russian citizens. 
. 
. sum citizenuk

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}citizenuk {c |}{res}      1,974    9.528875    1.276688          0         10
{txt}
{com}. sum citizenru

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}citizenru {c |}{res}      1,939    1.184631    2.325644          0         10
{txt}
{com}. 
. *Code for generating citizen bias dependent variable (already generated)
. *gen magcitizenbias = citizenuk-citizenru
. *gen dcitizenbias = 1 if magcitizenbias>0
. *replace dcitizenbias = 0 if magcitizenbias<1
. *replace dcitizenbias =. if magcitizenbias==.
. *tab dcitizenbias
. 
. *Figure 3.1 illustrates the distribution of responses on the 11-point scale where the modal response for closeness to ethnic Ukrainians is 10=very close (65%) and closeness to ethnic Russians is 0=not close at all (33.6%).
. 
. tab ethnicuk

    {txt}S3.c). Ethnic {c |}
    Ukrainians in {c |}
          Ukraine {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
 not close at all {c |}{res}         36        1.87        1.87
{txt}                1 {c |}{res}          7        0.36        2.24
{txt}                2 {c |}{res}         14        0.73        2.97
{txt}                3 {c |}{res}         17        0.88        3.85
{txt}                4 {c |}{res}         21        1.09        4.94
{txt}                5 {c |}{res}        126        6.56       11.50
{txt}                6 {c |}{res}         38        1.98       13.48
{txt}                7 {c |}{res}         68        3.54       17.01
{txt}                8 {c |}{res}        186        9.68       26.69
{txt}                9 {c |}{res}        156        8.12       34.81
{txt}       very close {c |}{res}      1,253       65.19      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,922      100.00
{txt}
{com}. tab ethnicru

    {txt}S3.d). Ethnic {c |}
      Russians in {c |}
          Ukraine {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
 not close at all {c |}{res}        632       33.63       33.63
{txt}                1 {c |}{res}         85        4.52       38.16
{txt}                2 {c |}{res}        106        5.64       43.80
{txt}                3 {c |}{res}        118        6.28       50.08
{txt}                4 {c |}{res}         82        4.36       54.44
{txt}                5 {c |}{res}        340       18.09       72.54
{txt}                6 {c |}{res}         49        2.61       75.15
{txt}                7 {c |}{res}         85        4.52       79.67
{txt}                8 {c |}{res}        114        6.07       85.74
{txt}                9 {c |}{res}         50        2.66       88.40
{txt}       very close {c |}{res}        218       11.60      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,879      100.00
{txt}
{com}. 
. *Figure 3.2 takes the difference in closeness to ethnic Ukrainians minus ethnic Russians. The distributions are skewed heavily toward a Ukrainian bias (scores greater than zero; 79%). In contrast, only a small minority report no difference in closeness across ethnicity (score equal to zero; 18%), while very few feel closer to ethnic Russians than Ukrainians (scores less than zero; 2.6%). 
. 
. tab dukbias

    {txt}dukbias {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        381       20.80       20.80
{txt}          1 {c |}{res}      1,451       79.20      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,832      100.00
{txt}
{com}. tab magukbias

  {txt}magukbias {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        -10 {c |}{res}          1        0.05        0.05
{txt}         -8 {c |}{res}          1        0.05        0.11
{txt}         -7 {c |}{res}          3        0.16        0.27
{txt}         -6 {c |}{res}          4        0.22        0.49
{txt}         -5 {c |}{res}          7        0.38        0.87
{txt}         -4 {c |}{res}          7        0.38        1.26
{txt}         -3 {c |}{res}          4        0.22        1.47
{txt}         -2 {c |}{res}          7        0.38        1.86
{txt}         -1 {c |}{res}         14        0.76        2.62
{txt}          0 {c |}{res}        333       18.18       20.80
{txt}          1 {c |}{res}         67        3.66       24.45
{txt}          2 {c |}{res}        120        6.55       31.00
{txt}          3 {c |}{res}         94        5.13       36.14
{txt}          4 {c |}{res}         87        4.75       40.88
{txt}          5 {c |}{res}        297       16.21       57.10
{txt}          6 {c |}{res}         70        3.82       60.92
{txt}          7 {c |}{res}        114        6.22       67.14
{txt}          8 {c |}{res}        128        6.99       74.13
{txt}          9 {c |}{res}         72        3.93       78.06
{txt}         10 {c |}{res}        402       21.94      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,832      100.00
{txt}
{com}. 
. *Differences are starker in Figure 3.3 when comparing social distance toward citizens of Ukraine (81% very close) relative to citizens of Russia (69% not close at all). 
. 
. tab citizenuk

  {txt}S2.a). Citizens {c |}
       of Ukraine {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
 not close at all {c |}{res}          6        0.30        0.30
{txt}                1 {c |}{res}          3        0.15        0.46
{txt}                2 {c |}{res}          2        0.10        0.56
{txt}                3 {c |}{res}          6        0.30        0.86
{txt}                4 {c |}{res}          4        0.20        1.06
{txt}                5 {c |}{res}         42        2.13        3.19
{txt}                6 {c |}{res}         16        0.81        4.00
{txt}                7 {c |}{res}         33        1.67        5.67
{txt}                8 {c |}{res}        127        6.43       12.11
{txt}                9 {c |}{res}        134        6.79       18.90
{txt}       very close {c |}{res}      1,601       81.10      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,974      100.00
{txt}
{com}. tab citizenru

  {txt}S2.b). Citizens {c |}
        of Russia {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
 not close at all {c |}{res}      1,342       69.21       69.21
{txt}                1 {c |}{res}        127        6.55       75.76
{txt}                2 {c |}{res}        125        6.45       82.21
{txt}                3 {c |}{res}         77        3.97       86.18
{txt}                4 {c |}{res}         41        2.11       88.29
{txt}                5 {c |}{res}        118        6.09       94.38
{txt}                6 {c |}{res}         14        0.72       95.10
{txt}                7 {c |}{res}         21        1.08       96.18
{txt}                8 {c |}{res}         14        0.72       96.91
{txt}                9 {c |}{res}          8        0.41       97.32
{txt}       very close {c |}{res}         52        2.68      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,939      100.00
{txt}
{com}. 
. *Figure 3.4 shows the data are heavily skewed toward extreme polarization in closeness to Ukrainian citizens and distance toward citizens of Russia (96% report feeling closer to Ukrainian citizens than Russian citizens with only 3% indicating equalitarian feelings of closeness and <1% feel closer to Russian citizens than Ukrainian citizens). 
. 
. tab dcitizenbias

{txt}dcitizenbia {c |}
          s {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         77        4.00        4.00
{txt}          1 {c |}{res}      1,847       96.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,924      100.00
{txt}
{com}. tab magcitizenbias

{txt}magcitizenb {c |}
        ias {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        -10 {c |}{res}          1        0.05        0.05
{txt}         -6 {c |}{res}          1        0.05        0.10
{txt}         -5 {c |}{res}          1        0.05        0.16
{txt}         -4 {c |}{res}          1        0.05        0.21
{txt}         -3 {c |}{res}          4        0.21        0.42
{txt}         -1 {c |}{res}          5        0.26        0.68
{txt}          0 {c |}{res}         64        3.33        4.00
{txt}          1 {c |}{res}         17        0.88        4.89
{txt}          2 {c |}{res}         13        0.68        5.56
{txt}          3 {c |}{res}         28        1.46        7.02
{txt}          4 {c |}{res}         31        1.61        8.63
{txt}          5 {c |}{res}        138        7.17       15.80
{txt}          6 {c |}{res}         70        3.64       19.44
{txt}          7 {c |}{res}         97        5.04       24.48
{txt}          8 {c |}{res}        168        8.73       33.21
{txt}          9 {c |}{res}        161        8.37       41.58
{txt}         10 {c |}{res}      1,124       58.42      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,924      100.00
{txt}
{com}. 
. *Though few respondents reported personal injury or sexual assault (1%), one-third (34%) indicated that family or close friends had been killed, 14% had moved or resettled since the war began, and 12% indicated that their homes or property had been damaged or destroyed in fighting since Russia's February 2022 invasion. 
. 
. tab injured 

               {txt}S1.a). Were you wounded? {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,964       98.20       98.20
{txt}                 Yes, by Russian Forces {c |}{res}         14        0.70       98.90
{txt}              Yes, by Ukrainian  Forces {c |}{res}          1        0.05       98.95
{txt}�Yes, by both Russian and Ukrainian for {c |}{res}          3        0.15       99.10
{txt}Yes, by another group or unsure by whom {c |}{res}          9        0.45       99.55
{txt}                            HARD TO SAY {c |}{res}          4        0.20       99.75
{txt}                      REFUSAL TO ANSWER {c |}{res}          5        0.25      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. tab sexassault  

  {txt}S1.c). Were any of your close friends {c |}
   or family members sexually abused or {c |}
                                  assau {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,951       97.55       97.55
{txt}                 Yes, by Russian Forces {c |}{res}         22        1.10       98.65
{txt}              Yes, by Ukrainian  Forces {c |}{res}          3        0.15       98.80
{txt}Yes, by another group or unsure by whom {c |}{res}          2        0.10       98.90
{txt}                            HARD TO SAY {c |}{res}         18        0.90       99.80
{txt}                      REFUSAL TO ANSWER {c |}{res}          4        0.20      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. tab faminjured 

  {txt}S1.b). Were any of your close friends {c |}
   or family members wounded or killed? {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,313       65.65       65.65
{txt}                 Yes, by Russian Forces {c |}{res}        596       29.80       95.45
{txt}              Yes, by Ukrainian  Forces {c |}{res}          4        0.20       95.65
{txt}�Yes, by both Russian and Ukrainian for {c |}{res}         12        0.60       96.25
{txt}Yes, by another group or unsure by whom {c |}{res}         45        2.25       98.50
{txt}                            HARD TO SAY {c |}{res}         25        1.25       99.75
{txt}                      REFUSAL TO ANSWER {c |}{res}          5        0.25      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. tab homedestroyed

       {txt}S1 d). Was your home or property {c |}
                  damaged or destroyed? {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                     No {c |}{res}      1,747       87.35       87.35
{txt}                 Yes, by Russian Forces {c |}{res}        182        9.10       96.45
{txt}              Yes, by Ukrainian  Forces {c |}{res}          3        0.15       96.60
{txt}�Yes, by both Russian and Ukrainian for {c |}{res}          9        0.45       97.05
{txt}Yes, by another group or unsure by whom {c |}{res}         39        1.95       99.00
{txt}                            HARD TO SAY {c |}{res}         17        0.85       99.85
{txt}                      REFUSAL TO ANSWER {c |}{res}          3        0.15      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      2,000      100.00
{txt}
{com}. 
. *We focus on victimization experiences at the hands of Russian forces exclusively in our analysis because few respondents reported victimization by Ukrainian forces or unknown perpetrators (<4%). In contrast, 36.3% indicated some form of direct victimization by Russian forces. Our measure of direct victimization experiences consists of an additive index of responses to victimization at the hands of Russian forces, which is coded 0 for 63.7% who report no victimization experiences, coded 1 for 32.3% who report 1 experience, coded 2 for 3.7% who report 2 experiences, and 3 for 0.7% who report 3 distinct experiences.
. 
. tab addrvictimindex

{txt}addrvictimi {c |}
       ndex {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,274       63.70       63.70
{txt}          1 {c |}{res}        645       32.25       95.95
{txt}          2 {c |}{res}         74        3.70       99.65
{txt}          3 {c |}{res}          7        0.35      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,000      100.00
{txt}
{com}. tab drvictimindex

{txt}drvictimind {c |}
         ex {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,274       63.70       63.70
{txt}          1 {c |}{res}        726       36.30      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,000      100.00
{txt}
{com}. 
. *…the effect size is relatively small for reducing both distance between Ukrainians and Russians by ethnicity (mean difference with no priming=5.17, SD=3.88, mean with priming=4.83, SD=3.86, two-sample t-test = -1.88, p<0.03, Cohen's d = 0.09)…
. 
. sum magukbias if victimorder==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}magukbias {c |}{res}        916    5.173581    3.880651         -7         10
{txt}
{com}. sum magukbias if victimorder==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}magukbias {c |}{res}        916    4.832969    3.864854        -10         10
{txt}
{com}. ttest magukbias, by(victimorder) unpaired unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    916{col 22} 5.173581{col 34} .1282203{col 46} 3.880651{col 58} 4.921941{col 70} 5.425221
       {txt}1 {c |}{res}{col 12}    916{col 22} 4.832969{col 34} .1276984{col 46} 3.864854{col 58} 4.582354{col 70} 5.083585
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,832{col 22} 5.003275{col 34} .0905439{col 46} 3.875449{col 58} 4.825695{col 70} 5.180855
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .3406114{col 34} .1809622{col 58}-.0143028{col 70} .6955255
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  1.8822
{txt}H0: diff = 0                     Satterthwaite's degrees of freedom = {res} 1829.97

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9700         {txt}Pr(|T| > |t|) = {res}0.0600          {txt}Pr(T > t) = {res}0.0300
{txt}
{com}. esize twosample magukbias, by(victimorder)

{txt}Effect size based on mean comparison

                               Obs per group:
                              victimorder==0 =        916
                              victimorder==1 =        916
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2} .0879505{col 34}{space 3}-.0036889{col 46}{space 3} .1795659
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .0879145{col 34}{space 3}-.0036874{col 46}{space 3} .1794923
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. 
. *…and citizenship (mean difference with no priming=8.45, SD=2.61, mean difference with priming=8.21, SD=2.85, two-sample t-test = -1.94, p<0.03, Cohen's d = 0.09).
. 
. sum magcitizenbias if victimorder==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
magcitizen~s {c |}{res}        966    8.452381    2.610324         -6         10
{txt}
{com}. sum magcitizenbias if victimorder==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
magcitizen~s {c |}{res}        958    8.210856    2.850399        -10         10
{txt}
{com}. ttest magcitizenbias, by(victimorder) unpaired unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    966{col 22} 8.452381{col 34} .0839858{col 46} 2.610324{col 58} 8.287565{col 70} 8.617197
       {txt}1 {c |}{res}{col 12}    958{col 22} 8.210856{col 34} .0920922{col 46} 2.850399{col 58}  8.03013{col 70} 8.391582
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,924{col 22} 8.332121{col 34} .0623403{col 46} 2.734458{col 58} 8.209859{col 70} 8.454382
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}  .241525{col 34} .1246378{col 58} -.002916{col 70}  .485966
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  1.9378
{txt}H0: diff = 0                     Satterthwaite's degrees of freedom = {res} 1904.43

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9736         {txt}Pr(|T| > |t|) = {res}0.0528          {txt}Pr(T > t) = {res}0.0264
{txt}
{com}. esize twosample magcitizenbias, by(victimorder)

{txt}Effect size based on mean comparison

                               Obs per group:
                              victimorder==0 =        966
                              victimorder==1 =        958
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2} .0883898{col 34}{space 3}-.0010328{col 46}{space 3} .1777894
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .0883553{col 34}{space 3}-.0010324{col 46}{space 3}   .17772
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. 
. *Also, in contrast to our preregistered expectations, we find no significant effects of victimization experience or interactions between our victimization prime and direct victimization experience on either ethnic bias (joint Wald F-test = 1.14, p<0.35) or citizenship-based bias (joint Wald F-test = 1.39, p<0.23).
. 
. reg magukbias victimorder##i.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,832
                                                {txt}F(7, 1824)        =  {res}     1.27
                                                {txt}Prob > F          = {res}    0.2596
                                                {txt}R-squared         = {res}    0.0046
                                                {txt}Root MSE          =    {res} 3.8739

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.4287272{col 41}{space 2} .2352291{col 52}{space 1}   -1.82{col 61}{space 3}0.069{col 69}{space 4}-.8900739{col 82}{space 3} .0326196
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .2601223{col 41}{space 2} .2638236{col 52}{space 1}    0.99{col 61}{space 3}0.324{col 69}{space 4}-.2573057{col 82}{space 3} .7775504
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.1195058{col 41}{space 2}  .668843{col 52}{space 1}   -0.18{col 61}{space 3}0.858{col 69}{space 4}-1.431284{col 82}{space 3} 1.192273
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-1.426523{col 41}{space 2}    1.917{col 52}{space 1}   -0.74{col 61}{space 3}0.457{col 69}{space 4}-5.186269{col 82}{space 3} 2.333222
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .2187972{col 41}{space 2} .3789719{col 52}{space 1}    0.58{col 61}{space 3}0.564{col 69}{space 4}-.5244672{col 82}{space 3} .9620616
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2} .6836144{col 41}{space 2} .9657294{col 52}{space 1}    0.71{col 61}{space 3}0.479{col 69}{space 4}-1.210437{col 82}{space 3} 2.577666
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2}  2.51206{col 41}{space 2} 2.551475{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4}-2.492059{col 82}{space 3}  7.51618
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}  5.09319{col 41}{space 2} .1712857{col 52}{space 1}   29.74{col 61}{space 3}0.000{col 69}{space 4} 4.757253{col 82}{space 3} 5.429127
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. contrast i.victimorder@i.addrvictimindex, effects
{res}
{txt}Contrasts of marginal linear predictions

{txt}{p2colset 1 10 10 2}{...}
{p2col:Margins:}{res:asbalanced}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 28}{c TT}{hline 11}{hline 12}{hline 11}
{col 29}{text}{c |}         df{col 41}           F{col 53}        P>F
{res}{col 1}{text}{hline 28}{c +}{hline 11}{hline 12}{hline 11}
victimorder@addrvictimindex {c |}
{space 25}0  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     3.32{col 53}{space 2}   0.0685
{txt}{space 25}1  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.50{col 53}{space 2}   0.4800
{txt}{space 25}2  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.07{col 53}{space 2}   0.7856
{txt}{space 25}3  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.67{col 53}{space 2}   0.4123
{col 1}{text}                     Joint {col 29}{c |}{result}{space 2}        4{col 41}{space 3}     1.14{col 53}{space 2}   0.3350
{col 29}{text}{c |}
{res}{col 1}{text}                Denominator{col 29}{c |}{result}{space 2}     1824
{col 1}{text}{hline 28}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}   Contrast{col 41}   Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
victimorder@addrvictimindex {c |}
{space 13}(1 vs base) 0  {c |}{col 29}{res}{space 2}-.4287272{col 41}{space 2} .2352291{col 52}{space 1}   -1.82{col 61}{space 3}0.069{col 69}{space 4}-.8900739{col 82}{space 3} .0326196
{txt}{space 13}(1 vs base) 1  {c |}{col 29}{res}{space 2}-.2099299{col 41}{space 2} .2971312{col 52}{space 1}   -0.71{col 61}{space 3}0.480{col 69}{space 4}-.7926831{col 82}{space 3} .3728232
{txt}{space 13}(1 vs base) 2  {c |}{col 29}{res}{space 2} .2548872{col 41}{space 2} .9366432{col 52}{space 1}    0.27{col 61}{space 3}0.786{col 69}{space 4}-1.582119{col 82}{space 3} 2.091893
{txt}{space 13}(1 vs base) 3  {c |}{col 29}{res}{space 2} 2.083333{col 41}{space 2} 2.540608{col 52}{space 1}    0.82{col 61}{space 3}0.412{col 69}{space 4}-2.899474{col 82}{space 3} 7.066141
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. reg magcitizenbias victimorder##i.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,924
                                                {txt}F(7, 1916)        =  {res}     1.62
                                                {txt}Prob > F          = {res}    0.1254
                                                {txt}R-squared         = {res}    0.0073
                                                {txt}Root MSE          =    {res} 2.7294

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.3227291{col 41}{space 2} .1604647{col 52}{space 1}   -2.01{col 61}{space 3}0.044{col 69}{space 4} -.637433{col 82}{space 3}-.0080253
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .1612591{col 41}{space 2} .1728239{col 52}{space 1}    0.93{col 61}{space 3}0.351{col 69}{space 4}-.1776837{col 82}{space 3} .5002019
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.0858452{col 41}{space 2} .5417101{col 52}{space 1}   -0.16{col 61}{space 3}0.874{col 69}{space 4}-1.148249{col 82}{space 3} .9765582
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-3.410169{col 41}{space 2} 2.364414{col 52}{space 1}   -1.44{col 61}{space 3}0.149{col 69}{space 4}-8.047266{col 82}{space 3} 1.226927
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .2530306{col 41}{space 2} .2570289{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4}-.2510551{col 82}{space 3} .7571164
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2} .0539603{col 41}{space 2} .7555469{col 52}{space 1}    0.07{col 61}{space 3}0.943{col 69}{space 4} -1.42782{col 82}{space 3} 1.535741
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2} 3.322729{col 41}{space 2} 2.666602{col 52}{space 1}    1.25{col 61}{space 3}0.213{col 69}{space 4}-1.907018{col 82}{space 3} 8.552476
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.410169{col 41}{space 2} .1081786{col 52}{space 1}   77.74{col 61}{space 3}0.000{col 69}{space 4} 8.198009{col 82}{space 3}  8.62233
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. contrast i.victimorder@i.addrvictimindex, effects
{res}
{txt}Contrasts of marginal linear predictions

{txt}{p2colset 1 10 10 2}{...}
{p2col:Margins:}{res:asbalanced}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 28}{c TT}{hline 11}{hline 12}{hline 11}
{col 29}{text}{c |}         df{col 41}           F{col 53}        P>F
{res}{col 1}{text}{hline 28}{c +}{hline 11}{hline 12}{hline 11}
victimorder@addrvictimindex {c |}
{space 25}0  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     4.04{col 53}{space 2}   0.0444
{txt}{space 25}1  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.12{col 53}{space 2}   0.7285
{txt}{space 25}2  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     0.13{col 53}{space 2}   0.7159
{txt}{space 25}3  {res}{col 29}{text}{c |}{result}{space 2}        1{col 41}{space 3}     1.27{col 53}{space 2}   0.2599
{col 1}{text}                     Joint {col 29}{c |}{result}{space 2}        4{col 41}{space 3}     1.39{col 53}{space 2}   0.2342
{col 29}{text}{c |}
{res}{col 1}{text}                Denominator{col 29}{c |}{result}{space 2}     1916
{col 1}{text}{hline 28}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}   Contrast{col 41}   Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
victimorder@addrvictimindex {c |}
{space 13}(1 vs base) 0  {c |}{col 29}{res}{space 2}-.3227291{col 41}{space 2} .1604647{col 52}{space 1}   -2.01{col 61}{space 3}0.044{col 69}{space 4} -.637433{col 82}{space 3}-.0080253
{txt}{space 13}(1 vs base) 1  {c |}{col 29}{res}{space 2}-.0696985{col 41}{space 2} .2007858{col 52}{space 1}   -0.35{col 61}{space 3}0.729{col 69}{space 4}-.4634801{col 82}{space 3} .3240831
{txt}{space 13}(1 vs base) 2  {c |}{col 29}{res}{space 2}-.2687688{col 41}{space 2} .7383103{col 52}{space 1}   -0.36{col 61}{space 3}0.716{col 69}{space 4}-1.716745{col 82}{space 3} 1.179208
{txt}{space 13}(1 vs base) 3  {c |}{col 29}{res}{space 2}        3{col 41}{space 2} 2.661769{col 52}{space 1}    1.13{col 61}{space 3}0.260{col 69}{space 4}-2.220269{col 82}{space 3} 8.220269
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Note: for continuous victimization index, see also
. reg magukbias victimorder##c.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,832
                                                {txt}F(3, 1828)        =  {res}     2.29
                                                {txt}Prob > F          = {res}    0.0760
                                                {txt}R-squared         = {res}    0.0038
                                                {txt}Root MSE          =    {res} 3.8713

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}        magukbias{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.victimorder {c |}{col 19}{res}{space 2} -.454768{col 31}{space 2} .2280758{col 42}{space 1}   -1.99{col 51}{space 3}0.046{col 59}{space 4}-.9020845{col 72}{space 3}-.0074515
{txt}{space 2}addrvictimindex {c |}{col 19}{res}{space 2}  .088306{col 31}{space 2} .2163663{col 42}{space 1}    0.41{col 51}{space 3}0.683{col 59}{space 4}-.3360451{col 72}{space 3} .5126572
{txt}{space 17} {c |}
{space 6}victimorder#{c |}
c.addrvictimindex {c |}
{space 15}1  {c |}{col 19}{res}{space 2} .3073621{col 31}{space 2}  .305871{col 42}{space 1}    1.00{col 51}{space 3}0.315{col 59}{space 4}-.2925311{col 72}{space 3} .9072554
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} 5.134826{col 31}{space 2} .1658482{col 42}{space 1}   30.96{col 51}{space 3}0.000{col 59}{space 4} 4.809555{col 72}{space 3} 5.460098
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. testparm i.victimorder##c.addrvictimindex

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.victimorder = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} addrvictimindex = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} 1.victimorder#c.addrvictimindex = 0{p_end}

{txt}       F(  3,  1828) ={res}    2.29
{txt}{col 13}Prob > F ={res}    0.0760
{txt}
{com}. reg magcitizenbias victimorder##c.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,924
                                                {txt}F(3, 1920)        =  {res}     1.78
                                                {txt}Prob > F          = {res}    0.1497
                                                {txt}R-squared         = {res}    0.0030
                                                {txt}Root MSE          =    {res} 2.7325

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}   magcitizenbias{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.victimorder {c |}{col 19}{res}{space 2} -.326847{col 31}{space 2}  .157484{col 42}{space 1}   -2.08{col 51}{space 3}0.038{col 59}{space 4}-.6357047{col 72}{space 3}-.0179893
{txt}{space 2}addrvictimindex {c |}{col 19}{res}{space 2}-.0016632{col 31}{space 2} .1645041{col 42}{space 1}   -0.01{col 51}{space 3}0.992{col 59}{space 4}-.3242887{col 72}{space 3} .3209624
{txt}{space 17} {c |}
{space 6}victimorder#{c |}
c.addrvictimindex {c |}
{space 15}1  {c |}{col 19}{res}{space 2} .2189484{col 31}{space 2} .2281525{col 42}{space 1}    0.96{col 51}{space 3}0.337{col 59}{space 4}-.2285045{col 72}{space 3} .6664012
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} 8.453102{col 31}{space 2} .1074567{col 42}{space 1}   78.67{col 51}{space 3}0.000{col 59}{space 4} 8.242358{col 72}{space 3} 8.663846
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. testparm i.victimorder##c.addrvictimindex

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.victimorder = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} addrvictimindex = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} 1.victimorder#c.addrvictimindex = 0{p_end}

{txt}       F(  3,  1920) ={res}    1.78
{txt}{col 13}Prob > F ={res}    0.1497
{txt}
{com}. 
. *Figure 1
. 
. *Figure 1 (use Data_OMN_Nov_2021_KIIS_ukr.dta and do file)
. 
. *Figure 2
. 
. *Figure 2 (use Data_OMN_Kiis_prepostwar.dta and do file)
. 
. *Table 2
. 
. sum ethnicuk ethnicru magukbias dukbias

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}ethnicuk {c |}{res}      1,922    8.806452    2.186514          0         10
{txt}{space 4}ethnicru {c |}{res}      1,879    3.783928    3.541499          0         10
{txt}{space 3}magukbias {c |}{res}      1,832    5.003275    3.875449        -10         10
{txt}{space 5}dukbias {c |}{res}      1,832    .7920306    .4059656          0          1
{txt}
{com}. sum citizenuk citizenru magcitizenbias dcitizenbias

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}citizenuk {c |}{res}      1,974    9.528875    1.276688          0         10
{txt}{space 3}citizenru {c |}{res}      1,939    1.184631    2.325644          0         10
{txt}magcitizen~s {c |}{res}      1,924    8.332121    2.734458        -10         10
{txt}dcitizenbias {c |}{res}      1,924    .9599792    .1960589          0          1
{txt}
{com}. 
. *Figure 3
. 
. graph twoway (histogram ethnicuk, discrete percent) (histogram ethnicru, discrete percent)
{res}{txt}
{com}. graph save g1
{res}{txt}file {bf:g1.gph} saved

{com}. histogram magukbias, discrete percent
{txt}(start={res}-10{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g2
{res}{txt}file {bf:g2.gph} saved

{com}. graph combine "g1.gph" "g2.gph"
{res}{txt}
{com}. *Note additional formatting requires the "Figure 3.1-2 formatting.grec" file with the command graph play "Figure 3.1-2 formatting.grec" 
. 
. graph twoway (histogram citizenuk, discrete percent) (histogram citizenru, discrete percent)
{res}{txt}
{com}. graph save g3
{res}{txt}file {bf:g3.gph} saved

{com}. histogram magcitizenbias, discrete percent
{txt}(start={res}-10{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g4
{res}{txt}file {bf:g4.gph} saved

{com}. graph combine "g3.gph" "g4.gph"
{res}{txt}
{com}. *Note additional formatting requires the "Figure 3.3-4 formatting.grec" file with the command graph play "Figure 3.3-4 formatting.grec" 
. 
. *Table 3
. 
. reg magukbias victimorder##c.addrvictimindex, robust

{txt}Linear regression                               Number of obs     = {res}     1,832
                                                {txt}F(3, 1828)        =  {res}     2.29
                                                {txt}Prob > F          = {res}    0.0760
                                                {txt}R-squared         = {res}    0.0038
                                                {txt}Root MSE          =    {res} 3.8713

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}        magukbias{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.victimorder {c |}{col 19}{res}{space 2} -.454768{col 31}{space 2} .2280758{col 42}{space 1}   -1.99{col 51}{space 3}0.046{col 59}{space 4}-.9020845{col 72}{space 3}-.0074515
{txt}{space 2}addrvictimindex {c |}{col 19}{res}{space 2}  .088306{col 31}{space 2} .2163663{col 42}{space 1}    0.41{col 51}{space 3}0.683{col 59}{space 4}-.3360451{col 72}{space 3} .5126572
{txt}{space 17} {c |}
{space 6}victimorder#{c |}
c.addrvictimindex {c |}
{space 15}1  {c |}{col 19}{res}{space 2} .3073621{col 31}{space 2}  .305871{col 42}{space 1}    1.00{col 51}{space 3}0.315{col 59}{space 4}-.2925311{col 72}{space 3} .9072554
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} 5.134826{col 31}{space 2} .1658482{col 42}{space 1}   30.96{col 51}{space 3}0.000{col 59}{space 4} 4.809555{col 72}{space 3} 5.460098
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. vif

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
1.victimor~r {c |} {res}     1.50    0.668521
{txt}addrvictim~x {c |} {res}     1.98    0.505638
{txt}{space 1}victimorder#{c |}
{space 10}c. {c |}
addrvictim~x {c |}
{space 10}1  {c |} {res}     2.41    0.415347
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.96
{txt}
{com}. reg magukbias victimorder##c.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,800
                                                {txt}F(28, 1771)       =  {res}     7.97
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0909
                                                {txt}Root MSE          =    {res}   3.73

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.5139835{col 41}{space 2} .2247459{col 52}{space 1}   -2.29{col 61}{space 3}0.022{col 69}{space 4}-.9547786{col 82}{space 3}-.0731885
{txt}{space 12}addrvictimindex {c |}{col 29}{res}{space 2}-.3291565{col 41}{space 2} .3495727{col 52}{space 1}   -0.94{col 61}{space 3}0.347{col 69}{space 4}-1.014775{col 82}{space 3}  .356462
{txt}{space 27} {c |}
{space 16}victimorder#{c |}
{space 10}c.addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2}  .344544{col 41}{space 2} .3094566{col 52}{space 1}    1.11{col 61}{space 3}0.266{col 69}{space 4}-.2623945{col 82}{space 3} .9514826
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.2536484{col 41}{space 2} .1775974{col 52}{space 1}   -1.43{col 61}{space 3}0.153{col 69}{space 4}-.6019709{col 82}{space 3} .0946742
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2}-.1658737{col 41}{space 2} .9360499{col 52}{space 1}   -0.18{col 61}{space 3}0.859{col 69}{space 4}-2.001753{col 82}{space 3} 1.670005
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .3843843{col 41}{space 2}  .350319{col 52}{space 1}    1.10{col 61}{space 3}0.273{col 69}{space 4} -.302698{col 82}{space 3} 1.071467
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2} .2580429{col 41}{space 2}  .782793{col 52}{space 1}    0.33{col 61}{space 3}0.742{col 69}{space 4}-1.277253{col 82}{space 3} 1.793338
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .1924009{col 41}{space 2} .2898481{col 52}{space 1}    0.66{col 61}{space 3}0.507{col 69}{space 4}-.3760795{col 82}{space 3} .7608813
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .5889504{col 41}{space 2} .4886248{col 52}{space 1}    1.21{col 61}{space 3}0.228{col 69}{space 4}-.3693916{col 82}{space 3} 1.547292
{txt}{space 21}female {c |}{col 29}{res}{space 2} .5268387{col 41}{space 2} .1871091{col 52}{space 1}    2.82{col 61}{space 3}0.005{col 69}{space 4} .1598608{col 82}{space 3} .8938167
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0347146{col 41}{space 2} .0081037{col 52}{space 1}   -4.28{col 61}{space 3}0.000{col 69}{space 4}-.0506085{col 82}{space 3}-.0188207
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0467214{col 41}{space 2} .0642586{col 52}{space 1}   -0.73{col 61}{space 3}0.467{col 69}{space 4} -.172752{col 82}{space 3} .0793092
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-2.222608{col 41}{space 2} .5279473{col 52}{space 1}   -4.21{col 61}{space 3}0.000{col 69}{space 4}-3.258073{col 82}{space 3}-1.187143
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-1.017516{col 41}{space 2} .2626996{col 52}{space 1}   -3.87{col 61}{space 3}0.000{col 69}{space 4}-1.532749{col 82}{space 3}-.5022818
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} -.111272{col 41}{space 2} .3841832{col 52}{space 1}   -0.29{col 61}{space 3}0.772{col 69}{space 4}-.8647722{col 82}{space 3} .6422281
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .1335383{col 41}{space 2} .3168857{col 52}{space 1}    0.42{col 61}{space 3}0.674{col 69}{space 4}-.4879711{col 82}{space 3} .7550477
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2}-.9481921{col 41}{space 2} .4345737{col 52}{space 1}   -2.18{col 61}{space 3}0.029{col 69}{space 4}-1.800523{col 82}{space 3}-.0958608
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2}-.1606969{col 41}{space 2} .4275316{col 52}{space 1}   -0.38{col 61}{space 3}0.707{col 69}{space 4}-.9992165{col 82}{space 3} .6778227
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} -.102412{col 41}{space 2} .5666669{col 52}{space 1}   -0.18{col 61}{space 3}0.857{col 69}{space 4}-1.213818{col 82}{space 3} 1.008994
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0154685{col 41}{space 2} .4287643{col 52}{space 1}   -0.04{col 61}{space 3}0.971{col 69}{space 4}-.8564059{col 82}{space 3} .8254689
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .2840183{col 41}{space 2} .3507585{col 52}{space 1}    0.81{col 61}{space 3}0.418{col 69}{space 4} -.403926{col 82}{space 3} .9719625
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-1.244997{col 41}{space 2} .5117195{col 52}{space 1}   -2.43{col 61}{space 3}0.015{col 69}{space 4}-2.248635{col 82}{space 3}-.2413594
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.8572101{col 41}{space 2} .4046814{col 52}{space 1}   -2.12{col 61}{space 3}0.034{col 69}{space 4}-1.650914{col 82}{space 3}-.0635066
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .1209071{col 41}{space 2} .0851772{col 52}{space 1}    1.42{col 61}{space 3}0.156{col 69}{space 4}-.0461514{col 82}{space 3} .2879656
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .2510599{col 41}{space 2} .2310832{col 52}{space 1}    1.09{col 61}{space 3}0.277{col 69}{space 4}-.2021646{col 82}{space 3} .7042845
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.8125459{col 41}{space 2} .2324829{col 52}{space 1}   -3.50{col 61}{space 3}0.000{col 69}{space 4}-1.268516{col 82}{space 3}-.3565762
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-1.048276{col 41}{space 2} .2790894{col 52}{space 1}   -3.76{col 61}{space 3}0.000{col 69}{space 4}-1.595655{col 82}{space 3}-.5008968
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.811576{col 41}{space 2} .3610453{col 52}{space 1}   -5.02{col 61}{space 3}0.000{col 69}{space 4}-2.519696{col 82}{space 3}-1.103457
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 7.678015{col 41}{space 2} .8044666{col 52}{space 1}    9.54{col 61}{space 3}0.000{col 69}{space 4} 6.100211{col 82}{space 3} 9.255819
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. vif

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
1.victimor~r {c |} {res}     1.51    0.661028
{txt}addrvictim~x {c |} {res}     6.11    0.163755
{txt}{space 1}victimorder#{c |}
{space 10}c. {c |}
addrvictim~x {c |}
{space 10}1  {c |} {res}     2.51    0.398485
{txt}{space 1}ethnicorder {c |} {res}     1.02    0.984020
{txt}{space 2}1.rinjured {c |} {res}     1.16    0.861552
{txt}1.rfaminju~d {c |} {res}     4.11    0.243258
{txt}1.rsexassa~t {c |} {res}     1.24    0.805235
{txt}{space 7}moved {c |} {res}     1.46    0.684851
{txt}{space 4}occupied {c |} {res}     1.35    0.742152
{txt}{space 6}female {c |} {res}     1.16    0.861230
{txt}{space 9}age {c |} {res}     2.04    0.489174
{txt}{space 3}education {c |} {res}     1.33    0.752678
{txt}{space 5}russian {c |} {res}     1.08    0.922992
{txt}{space 2}russpeaker {c |} {res}     1.24    0.805356
{txt}{space 2}occupation {c |}
{space 10}2  {c |} {res}     1.52    0.657326
{txt}{space 10}3  {c |} {res}     2.27    0.441224
{txt}{space 10}4  {c |} {res}     1.34    0.748113
{txt}{space 10}5  {c |} {res}     1.36    0.734805
{txt}{space 10}6  {c |} {res}     1.19    0.840650
{txt}{space 10}7  {c |} {res}     1.47    0.678057
{txt}{space 10}8  {c |} {res}     2.72    0.367468
{txt}{space 10}9  {c |} {res}     1.31    0.762873
{txt}{space 9}10  {c |} {res}     1.44    0.693071
{txt}{space 6}income {c |} {res}     1.29    0.775196
{txt}{space 1}urban_rural {c |} {res}     1.10    0.906403
{txt}{space 5}region4 {c |}
{space 10}2  {c |} {res}     1.93    0.519381
{txt}{space 10}3  {c |} {res}     1.95    0.512811
{txt}{space 10}4  {c |} {res}     2.00    0.501024
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.79
{txt}
{com}. reg magcitizenbias victimorder##c.addrvictimindex, robust

{txt}Linear regression                               Number of obs     = {res}     1,924
                                                {txt}F(3, 1920)        =  {res}     1.78
                                                {txt}Prob > F          = {res}    0.1497
                                                {txt}R-squared         = {res}    0.0030
                                                {txt}Root MSE          =    {res} 2.7325

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}   magcitizenbias{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.victimorder {c |}{col 19}{res}{space 2} -.326847{col 31}{space 2}  .157484{col 42}{space 1}   -2.08{col 51}{space 3}0.038{col 59}{space 4}-.6357047{col 72}{space 3}-.0179893
{txt}{space 2}addrvictimindex {c |}{col 19}{res}{space 2}-.0016632{col 31}{space 2} .1645041{col 42}{space 1}   -0.01{col 51}{space 3}0.992{col 59}{space 4}-.3242887{col 72}{space 3} .3209624
{txt}{space 17} {c |}
{space 6}victimorder#{c |}
c.addrvictimindex {c |}
{space 15}1  {c |}{col 19}{res}{space 2} .2189484{col 31}{space 2} .2281525{col 42}{space 1}    0.96{col 51}{space 3}0.337{col 59}{space 4}-.2285045{col 72}{space 3} .6664012
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} 8.453102{col 31}{space 2} .1074567{col 42}{space 1}   78.67{col 51}{space 3}0.000{col 59}{space 4} 8.242358{col 72}{space 3} 8.663846
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. vif

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
1.victimor~r {c |} {res}     1.50    0.666647
{txt}addrvictim~x {c |} {res}     1.99    0.503471
{txt}{space 1}victimorder#{c |}
{space 10}c. {c |}
addrvictim~x {c |}
{space 10}1  {c |} {res}     2.43    0.411523
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.97
{txt}
{com}. reg magcitizenbias victimorder##c.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,883
                                                {txt}F(28, 1854)       =  {res}     4.22
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0772
                                                {txt}Root MSE          =    {res} 2.6492

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.3817418{col 41}{space 2} .1551378{col 52}{space 1}   -2.46{col 61}{space 3}0.014{col 69}{space 4}-.6860049{col 82}{space 3}-.0774787
{txt}{space 12}addrvictimindex {c |}{col 29}{res}{space 2}-.2933996{col 41}{space 2} .2861444{col 52}{space 1}   -1.03{col 61}{space 3}0.305{col 69}{space 4}-.8545987{col 82}{space 3} .2677996
{txt}{space 27} {c |}
{space 16}victimorder#{c |}
{space 10}c.addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .2414888{col 41}{space 2} .2340489{col 52}{space 1}    1.03{col 61}{space 3}0.302{col 69}{space 4}-.2175382{col 82}{space 3} .7005158
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.0791168{col 41}{space 2} .1221215{col 52}{space 1}   -0.65{col 61}{space 3}0.517{col 69}{space 4}-.3186268{col 82}{space 3} .1603932
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2} .4731684{col 41}{space 2} .7273582{col 52}{space 1}    0.65{col 61}{space 3}0.515{col 69}{space 4}-.9533588{col 82}{space 3} 1.899696
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .2639131{col 41}{space 2} .2610925{col 52}{space 1}    1.01{col 61}{space 3}0.312{col 69}{space 4}-.2481531{col 82}{space 3} .7759794
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2}  .063017{col 41}{space 2} .8567611{col 52}{space 1}    0.07{col 61}{space 3}0.941{col 69}{space 4}-1.617301{col 82}{space 3} 1.743335
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .4549538{col 41}{space 2} .1969918{col 52}{space 1}    2.31{col 61}{space 3}0.021{col 69}{space 4} .0686048{col 82}{space 3} .8413028
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .5248018{col 41}{space 2} .3437038{col 52}{space 1}    1.53{col 61}{space 3}0.127{col 69}{space 4}-.1492855{col 82}{space 3} 1.198889
{txt}{space 21}female {c |}{col 29}{res}{space 2} .3603791{col 41}{space 2} .1401639{col 52}{space 1}    2.57{col 61}{space 3}0.010{col 69}{space 4} .0854835{col 82}{space 3} .6352747
{txt}{space 24}age {c |}{col 29}{res}{space 2} .0001514{col 41}{space 2} .0054364{col 52}{space 1}    0.03{col 61}{space 3}0.978{col 69}{space 4}-.0105107{col 82}{space 3} .0108134
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0320187{col 41}{space 2} .0451276{col 52}{space 1}   -0.71{col 61}{space 3}0.478{col 69}{space 4}-.1205249{col 82}{space 3} .0564875
{txt}{space 20}russian {c |}{col 29}{res}{space 2} -1.68252{col 41}{space 2} .5453788{col 52}{space 1}   -3.09{col 61}{space 3}0.002{col 69}{space 4}-2.752141{col 82}{space 3}-.6128995
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-.9283514{col 41}{space 2} .2056766{col 52}{space 1}   -4.51{col 61}{space 3}0.000{col 69}{space 4}-1.331733{col 82}{space 3}-.5249694
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .0891802{col 41}{space 2} .2699548{col 52}{space 1}    0.33{col 61}{space 3}0.741{col 69}{space 4}-.4402671{col 82}{space 3} .6186275
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .0516484{col 41}{space 2} .2351451{col 52}{space 1}    0.22{col 61}{space 3}0.826{col 69}{space 4}-.4095287{col 82}{space 3} .5128254
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .0953737{col 41}{space 2} .3017433{col 52}{space 1}    0.32{col 61}{space 3}0.752{col 69}{space 4}-.4964186{col 82}{space 3}  .687166
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .2385688{col 41}{space 2} .2978126{col 52}{space 1}    0.80{col 61}{space 3}0.423{col 69}{space 4}-.3455146{col 82}{space 3} .8226521
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .7936211{col 41}{space 2} .3487294{col 52}{space 1}    2.28{col 61}{space 3}0.023{col 69}{space 4} .1096777{col 82}{space 3} 1.477565
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0833139{col 41}{space 2}  .315588{col 52}{space 1}   -0.26{col 61}{space 3}0.792{col 69}{space 4}-.7022589{col 82}{space 3} .5356312
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .1435771{col 41}{space 2} .2457987{col 52}{space 1}    0.58{col 61}{space 3}0.559{col 69}{space 4}-.3384942{col 82}{space 3} .6256483
{txt}{space 19}Student  {c |}{col 29}{res}{space 2} .0720239{col 41}{space 2}  .370912{col 52}{space 1}    0.19{col 61}{space 3}0.846{col 69}{space 4}-.6554251{col 82}{space 3} .7994728
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.4089573{col 41}{space 2} .3077993{col 52}{space 1}   -1.33{col 61}{space 3}0.184{col 69}{space 4}-1.012627{col 82}{space 3} .1947123
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .0897034{col 41}{space 2} .0607802{col 52}{space 1}    1.48{col 61}{space 3}0.140{col 69}{space 4}-.0295014{col 82}{space 3} .2089082
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2}  .206647{col 41}{space 2} .1522301{col 52}{space 1}    1.36{col 61}{space 3}0.175{col 69}{space 4}-.0919133{col 82}{space 3} .5052074
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.0692258{col 41}{space 2} .1588913{col 52}{space 1}   -0.44{col 61}{space 3}0.663{col 69}{space 4}-.3808504{col 82}{space 3} .2423989
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-.1930616{col 41}{space 2} .1974793{col 52}{space 1}   -0.98{col 61}{space 3}0.328{col 69}{space 4}-.5803668{col 82}{space 3} .1942436
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.064271{col 41}{space 2} .2642194{col 52}{space 1}   -4.03{col 61}{space 3}0.000{col 69}{space 4} -1.58247{col 82}{space 3}-.5460719
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.387859{col 41}{space 2} .5522442{col 52}{space 1}   15.19{col 61}{space 3}0.000{col 69}{space 4} 7.304773{col 82}{space 3} 9.470945
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. vif

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
1.victimor~r {c |} {res}     1.52    0.658746
{txt}addrvictim~x {c |} {res}     6.00    0.166582
{txt}{space 1}victimorder#{c |}
{space 10}c. {c |}
addrvictim~x {c |}
{space 10}1  {c |} {res}     2.52    0.396599
{txt}{space 1}ethnicorder {c |} {res}     1.01    0.986168
{txt}{space 2}1.rinjured {c |} {res}     1.16    0.865199
{txt}1.rfaminju~d {c |} {res}     4.08    0.244905
{txt}1.rsexassa~t {c |} {res}     1.23    0.810662
{txt}{space 7}moved {c |} {res}     1.45    0.689591
{txt}{space 4}occupied {c |} {res}     1.34    0.744932
{txt}{space 6}female {c |} {res}     1.16    0.863456
{txt}{space 9}age {c |} {res}     2.07    0.484176
{txt}{space 3}education {c |} {res}     1.33    0.751080
{txt}{space 5}russian {c |} {res}     1.08    0.928664
{txt}{space 2}russpeaker {c |} {res}     1.23    0.814843
{txt}{space 2}occupation {c |}
{space 10}2  {c |} {res}     1.51    0.661145
{txt}{space 10}3  {c |} {res}     2.23    0.447592
{txt}{space 10}4  {c |} {res}     1.32    0.757739
{txt}{space 10}5  {c |} {res}     1.34    0.744669
{txt}{space 10}6  {c |} {res}     1.18    0.848650
{txt}{space 10}7  {c |} {res}     1.46    0.684133
{txt}{space 10}8  {c |} {res}     2.71    0.368841
{txt}{space 10}9  {c |} {res}     1.31    0.762862
{txt}{space 9}10  {c |} {res}     1.46    0.684473
{txt}{space 6}income {c |} {res}     1.29    0.777409
{txt}{space 1}urban_rural {c |} {res}     1.11    0.902188
{txt}{space 5}region4 {c |}
{space 10}2  {c |} {res}     1.93    0.519477
{txt}{space 10}3  {c |} {res}     1.94    0.514331
{txt}{space 10}4  {c |} {res}     1.99    0.503261
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.78
{txt}
{com}. 
. *Table 4
. 
. reg revethnicuk victimorder, robust

{txt}Linear regression                               Number of obs     = {res}     1,922
                                                {txt}F(1, 1920)        =  {res}     0.15
                                                {txt}Prob > F          = {res}    0.7004
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res}  2.187

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} revethnicuk{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}-.0384035{col 26}{space 2} .0997941{col 37}{space 1}   -0.38{col 46}{space 3}0.700{col 54}{space 4}-.2341197{col 67}{space 3} .1573127
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.21259{col 26}{space 2} .0694041{col 37}{space 1}   31.88{col 46}{space 3}0.000{col 54}{space 4} 2.076475{col 67}{space 3} 2.348706
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revethnicru victimorder, robust

{txt}Linear regression                               Number of obs     = {res}     1,879
                                                {txt}F(1, 1877)        =  {res}     7.90
                                                {txt}Prob > F          = {res}    0.0050
                                                {txt}R-squared         = {res}    0.0042
                                                {txt}Root MSE          =    {res}  3.535

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} revethnicru{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}-.4585273{col 26}{space 2} .1631372{col 37}{space 1}   -2.81{col 46}{space 3}0.005{col 54}{space 4}-.7784765{col 67}{space 3} -.138578
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 7.443506{col 26}{space 2} .1135153{col 37}{space 1}   65.57{col 46}{space 3}0.000{col 54}{space 4} 7.220876{col 67}{space 3} 7.666135
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revcitizenuk victimorder, robust

{txt}Linear regression                               Number of obs     = {res}     1,974
                                                {txt}F(1, 1972)        =  {res}     0.22
                                                {txt}Prob > F          = {res}    0.6421
                                                {txt}R-squared         = {res}    0.0001
                                                {txt}Root MSE          =    {res} 1.2769

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revcitizenuk{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .0267153{col 26}{space 2} .0574738{col 37}{space 1}    0.46{col 46}{space 3}0.642{col 54}{space 4}-.0860004{col 67}{space 3} .1394311
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.457916{col 26}{space 2} .0407683{col 37}{space 1}   35.76{col 46}{space 3}0.000{col 54}{space 4} 1.377962{col 67}{space 3} 1.537869
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revcitizenru victimorder, robust

{txt}Linear regression                               Number of obs     = {res}     1,939
                                                {txt}F(1, 1937)        =  {res}     4.36
                                                {txt}Prob > F          = {res}    0.0370
                                                {txt}R-squared         = {res}    0.0022
                                                {txt}Root MSE          =    {res} 2.3236

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revcitizenru{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}-.2203881{col 26}{space 2} .1055815{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4}-.4274533{col 67}{space 3}-.0133229
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 9.925051{col 26}{space 2} .0711647{col 37}{space 1}  139.47{col 46}{space 3}0.000{col 54}{space 4} 9.785484{col 67}{space 3} 10.06462
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Robustness Check – Contextualizing Identity
. 
. *Use CC Ukraine Jan 2023 replication data.dta and do file
. 
. *Figure 4
. 
. *Use "CC Ukraine Jan 2023 Figure 4 data.dta" and do file
. 
. *Sample Demographics (July 2022)
. 
. sum victimorder i.injured i.faminjured i.sexassault i.homedestroyed moved occupied female age education i.nationality i.language i.surveylang i.occupation income rural i.region4

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}victimorder {c |}{res}      2,000       .4945    .5000948          0          1
{txt}{space 12} {c |}
{space 5}injured {c |}
{space 9}No  {c |}{res}      2,000        .982    .1329844          0          1
{txt}Yes, by R..  {c |}{res}      2,000        .007    .0833935          0          1
{txt}Yes, by U..  {c |}{res}      2,000       .0005    .0223607          0          1
{txt}�Yes, by ..  {c |}{res}      2,000       .0015    .0387105          0          1
{txt}{hline 13}{c +}{hline 57}
Yes, by a..  {c |}{res}      2,000       .0045    .0669477          0          1
{txt}HARD TO SAY  {c |}{res}      2,000        .002    .0446878          0          1
{txt}REFUSAL T..  {c |}{res}      2,000       .0025    .0499499          0          1
{txt}{space 12} {c |}
{space 2}faminjured {c |}
{space 9}No  {c |}{res}      2,000       .6565    .4749953          0          1
{txt}Yes, by R..  {c |}{res}      2,000        .298    .4574939          0          1
{txt}{hline 13}{c +}{hline 57}
Yes, by U..  {c |}{res}      2,000        .002    .0446878          0          1
{txt}�Yes, by ..  {c |}{res}      2,000        .006    .0772463          0          1
{txt}Yes, by a..  {c |}{res}      2,000       .0225      .14834          0          1
{txt}HARD TO SAY  {c |}{res}      2,000       .0125    .1111302          0          1
{txt}REFUSAL T..  {c |}{res}      2,000       .0025    .0499499          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
{space 2}sexassault {c |}
{space 9}No  {c |}{res}      2,000       .9755    .1546341          0          1
{txt}Yes, by R..  {c |}{res}      2,000        .011    .1043285          0          1
{txt}Yes, by U..  {c |}{res}      2,000       .0015    .0387105          0          1
{txt}Yes, by a..  {c |}{res}      2,000        .001    .0316149          0          1
{txt}HARD TO SAY  {c |}{res}      2,000        .009    .0944641          0          1
{txt}{hline 13}{c +}{hline 57}
REFUSAL T..  {c |}{res}      2,000        .002    .0446878          0          1
{txt}{space 12} {c |}
homedestro~d {c |}
{space 9}No  {c |}{res}      2,000       .8735    .3324952          0          1
{txt}Yes, by R..  {c |}{res}      2,000        .091     .287681          0          1
{txt}Yes, by U..  {c |}{res}      2,000       .0015    .0387105          0          1
{txt}�Yes, by ..  {c |}{res}      2,000       .0045    .0669477          0          1
{txt}{hline 13}{c +}{hline 57}
Yes, by a..  {c |}{res}      2,000       .0195    .1383088          0          1
{txt}HARD TO SAY  {c |}{res}      2,000       .0085    .0918257          0          1
{txt}REFUSAL T..  {c |}{res}      2,000       .0015    .0387105          0          1
{txt}{space 12} {c |}
{space 7}moved {c |}{res}      2,000       .1375    .3444606          0          1
{txt}{space 4}occupied {c |}{res}      2,000        .037     .188809          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}female {c |}{res}      2,000       .5375    .4987165          0          1
{txt}{space 9}age {c |}{res}      2,000      47.806     16.1338         18         93
{txt}{space 3}education {c |}{res}      1,995    6.435088    1.634069          1          8
{txt}{space 12} {c |}
{space 1}nationality {c |}
{space 2}Ukrainian  {c |}{res}      2,000        .944    .2299792          0          1
{txt}{space 4}Russian  {c |}{res}      2,000       .0245    .1546341          0          1
{txt}{hline 13}{c +}{hline 57}
Ukrainia..)  {c |}{res}      2,000       .0095    .0970281          0          1
{txt}{space 4}Belarus  {c |}{res}      2,000       .0005    .0223607          0          1
{txt}{space 2}Moldavian  {c |}{res}      2,000        .002    .0446878          0          1
{txt}Crimean T..  {c |}{res}      2,000       .0005    .0223607          0          1
{txt}{space 2}Bulgarian  {c |}{res}      2,000        .002    .0446878          0          1
{txt}{hline 13}{c +}{hline 57}
{space 2}Hungarian  {c |}{res}      2,000       .0005    .0223607          0          1
{txt}{space 7}Pole  {c |}{res}      2,000        .002    .0446878          0          1
{txt}{space 8}Jew  {c |}{res}      2,000        .002    .0446878          0          1
{txt}{space 6}Other  {c |}{res}      2,000        .007    .0833935          0          1
{txt}HARD TO SAY  {c |}{res}      2,000       .0055    .0739763          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
{space 4}language {c |}
{space 2}Ukrainian  {c |}{res}      2,000       .7295    .4443292          0          1
{txt}{space 4}Russian  {c |}{res}      2,000       .1965    .3974503          0          1
{txt}Equally, ..  {c |}{res}      2,000       .0325    .1773682          0          1
{txt}Hard to s..  {c |}{res}      2,000        .009    .0944641          0          1
{txt}Equally, ..  {c |}{res}      2,000        .022      .14672          0          1
{txt}{hline 13}{c +}{hline 57}
Hard to s..  {c |}{res}      2,000       .0105    .1019556          0          1
{txt}{space 12} {c |}
{space 2}surveylang {c |}
{space 2}UKRAINIAN  {c |}{res}      2,000        .643    .4792346          0          1
{txt}IN MOST C..  {c |}{res}      2,000        .065    .2465875          0          1
{txt}HALF ONE ..  {c |}{res}      2,000        .018    .1329844          0          1
{txt}A MIX OF..)  {c |}{res}      2,000        .034    .1812745          0          1
{txt}{hline 13}{c +}{hline 57}
IN MOST C..  {c |}{res}      2,000       .0315    .1747084          0          1
{txt}{space 1}IN RUSSIAN  {c |}{res}      2,000       .2085    .4063377          0          1
{txt}{space 12} {c |}
{space 2}occupation {c |}
Worker, f..  {c |}{res}      1,992    .1591365    .3658952          0          1
{txt}Servant ..)  {c |}{res}      1,992    .0848394    .2787125          0          1
{txt}Professi..)  {c |}{res}      1,992    .2088353    .4065786          0          1
{txt}{hline 13}{c +}{hline 57}
Self empl..  {c |}{res}      1,992    .0517068    .2214901          0          1
{txt}Entrepren..  {c |}{res}      1,992    .0532129     .224514          0          1
{txt}Military ..  {c |}{res}      1,992    .0225904    .1486308          0          1
{txt}Householder  {c |}{res}      1,992    .0692771    .2539885          0          1
{txt}Pension ..)  {c |}{res}      1,992    .2434739    .4292865          0          1
{txt}{hline 13}{c +}{hline 57}
{space 4}Student  {c |}{res}      1,992    .0316265    .1750476          0          1
{txt}{space 1}Unemployed  {c |}{res}      1,992    .0753012     .263943          0          1
{txt}{space 12} {c |}
{space 6}income {c |}{res}      1,959    3.420623    1.250823          1          7
{txt}{space 7}rural {c |}{res}      2,000        .198    .3985918          0          1
{txt}{space 12} {c |}
{space 5}region4 {c |}
{space 7}West  {c |}{res}      2,000        .186     .389204          0          1
{txt}{hline 13}{c +}{hline 57}
{space 4}Central  {c |}{res}      2,000        .432    .4954783          0          1
{txt}{space 6}South  {c |}{res}      2,000       .2475    .4316676          0          1
{txt}{space 7}East  {c |}{res}      2,000       .1345    .3412741          0          1
{txt}
{com}. 
. *Robustness Checks
. 
. *Manuscript Table 3 (Tobit Regression)
. 
. tobit magukbias victimorder##c.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, ll ul
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-4608.9202}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-4608.9202}  
Iteration 1:{space 2}Log likelihood = {res:-4535.6196}  
Iteration 2:{space 2}Log likelihood = {res:-4533.6933}  
Iteration 3:{space 2}Log likelihood = {res:-4533.6842}  
Iteration 4:{space 2}Log likelihood = {res:-4533.6842}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,800}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:1,404}
{txt}{col 1}Limits: Lower = {ralign 3:{res:-10}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:1}
{txt}{col 1}        Upper = {ralign 3:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:395}

{txt}{col 53}{lalign 17:LR chi2({res:28})}{col 70} = {res}{ralign 6:150.85}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-4533.6842}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0164}

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.6490526{col 41}{space 2} .2714202{col 52}{space 1}   -2.39{col 61}{space 3}0.017{col 69}{space 4} -1.18139{col 82}{space 3}-.1167151
{txt}{space 12}addrvictimindex {c |}{col 29}{res}{space 2}-.5384196{col 41}{space 2} .4633936{col 52}{space 1}   -1.16{col 61}{space 3}0.245{col 69}{space 4}-1.447275{col 82}{space 3} .3704361
{txt}{space 27} {c |}
{space 16}victimorder#{c |}
{space 10}c.addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .5207248{col 41}{space 2} .3835574{col 52}{space 1}    1.36{col 61}{space 3}0.175{col 69}{space 4}-.2315477{col 82}{space 3} 1.272997
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.2971965{col 41}{space 2} .2224382{col 52}{space 1}   -1.34{col 61}{space 3}0.182{col 69}{space 4}-.7334654{col 82}{space 3} .1390725
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2}-.2395352{col 41}{space 2} 1.500834{col 52}{space 1}   -0.16{col 61}{space 3}0.873{col 69}{space 4}-3.183126{col 82}{space 3} 2.704056
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .4561122{col 41}{space 2} .4838159{col 52}{space 1}    0.94{col 61}{space 3}0.346{col 69}{space 4}-.4927976{col 82}{space 3} 1.405022
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2} .2479735{col 41}{space 2} 1.162983{col 52}{space 1}    0.21{col 61}{space 3}0.831{col 69}{space 4}-2.032989{col 82}{space 3} 2.528936
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .1935381{col 41}{space 2} .3810626{col 52}{space 1}    0.51{col 61}{space 3}0.612{col 69}{space 4}-.5538414{col 82}{space 3} .9409176
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .5899594{col 41}{space 2}    .6535{col 52}{space 1}    0.90{col 61}{space 3}0.367{col 69}{space 4}-.6917526{col 82}{space 3} 1.871671
{txt}{space 21}female {c |}{col 29}{res}{space 2} .7280318{col 41}{space 2} .2378596{col 52}{space 1}    3.06{col 61}{space 3}0.002{col 69}{space 4} .2615169{col 82}{space 3} 1.194547
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0390407{col 41}{space 2} .0098591{col 52}{space 1}   -3.96{col 61}{space 3}0.000{col 69}{space 4}-.0583775{col 82}{space 3} -.019704
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0881529{col 41}{space 2} .0783034{col 52}{space 1}   -1.13{col 61}{space 3}0.260{col 69}{space 4}-.2417297{col 82}{space 3} .0654239
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-2.493477{col 41}{space 2} .7204713{col 52}{space 1}   -3.46{col 61}{space 3}0.001{col 69}{space 4} -3.90654{col 82}{space 3}-1.080414
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2} -1.19115{col 41}{space 2} .3071226{col 52}{space 1}   -3.88{col 61}{space 3}0.000{col 69}{space 4}-1.793511{col 82}{space 3}-.5887894
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2}-.0575654{col 41}{space 2} .4830966{col 52}{space 1}   -0.12{col 61}{space 3}0.905{col 69}{space 4}-1.005065{col 82}{space 3} .8899337
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .1838035{col 41}{space 2} .4069385{col 52}{space 1}    0.45{col 61}{space 3}0.652{col 69}{space 4}-.6143265{col 82}{space 3} .9819335
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2}-1.130779{col 41}{space 2} .5603931{col 52}{space 1}   -2.02{col 61}{space 3}0.044{col 69}{space 4} -2.22988{col 82}{space 3}-.0316779
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2}-.1684755{col 41}{space 2} .5662533{col 52}{space 1}   -0.30{col 61}{space 3}0.766{col 69}{space 4} -1.27907{col 82}{space 3} .9421192
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2}-.0662404{col 41}{space 2} .7801579{col 52}{space 1}   -0.08{col 61}{space 3}0.932{col 69}{space 4}-1.596367{col 82}{space 3} 1.463886
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}  .044164{col 41}{space 2} .5239607{col 52}{space 1}    0.08{col 61}{space 3}0.933{col 69}{space 4} -.983482{col 82}{space 3}  1.07181
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .3425948{col 41}{space 2} .4281603{col 52}{space 1}    0.80{col 61}{space 3}0.424{col 69}{space 4}-.4971575{col 82}{space 3} 1.182347
{txt}{space 19}Student  {c |}{col 29}{res}{space 2} -1.74415{col 41}{space 2} .7195943{col 52}{space 1}   -2.42{col 61}{space 3}0.015{col 69}{space 4}-3.155493{col 82}{space 3}-.3328066
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-1.062031{col 41}{space 2} .5045093{col 52}{space 1}   -2.11{col 61}{space 3}0.035{col 69}{space 4}-2.051527{col 82}{space 3} -.072535
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .1493316{col 41}{space 2} .1012904{col 52}{space 1}    1.47{col 61}{space 3}0.141{col 69}{space 4}-.0493296{col 82}{space 3} .3479929
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .3249081{col 41}{space 2} .2924021{col 52}{space 1}    1.11{col 61}{space 3}0.267{col 69}{space 4}-.2485811{col 82}{space 3} .8983973
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.9854467{col 41}{space 2} .3110474{col 52}{space 1}   -3.17{col 61}{space 3}0.002{col 69}{space 4}-1.595505{col 82}{space 3}-.3753884
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-1.259356{col 41}{space 2} .3591818{col 52}{space 1}   -3.51{col 61}{space 3}0.000{col 69}{space 4}-1.963821{col 82}{space 3}-.5548917
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-2.097337{col 41}{space 2} .4558566{col 52}{space 1}   -4.60{col 61}{space 3}0.000{col 69}{space 4} -2.99141{col 82}{space 3}-1.203264
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.785486{col 41}{space 2} .9678594{col 52}{space 1}    9.08{col 61}{space 3}0.000{col 69}{space 4}  6.88722{col 82}{space 3} 10.68375
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}var(e.magukbias){c |}{col 29}{res}{space 2} 20.81579{col 41}{space 2} .8335689{col 69}{space 4} 19.24346{col 82}{space 3} 22.51659
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. tobit magcitizenbias victimorder##c.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, ll ul
{res}{txt}
Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-3622.9264}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-3622.9264}  
Iteration 1:{space 2}Log likelihood = {res:-3154.2766}  
Iteration 2:{space 2}Log likelihood = {res:-3049.9496}  
Iteration 3:{space 2}Log likelihood = {res:-3034.0618}  
Iteration 4:{space 2}Log likelihood = {res: -3033.808}  
Iteration 5:{space 2}Log likelihood = {res:-3033.8071}  
Iteration 6:{space 2}Log likelihood = {res:-3033.8071}  
{res}
{txt}{col 1}Tobit regression{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:1,883}
{txt}{col 53}{ralign 17:Uncensored}{col 70} = {res}{ralign 6:782}
{txt}{col 1}Limits: Lower = {ralign 3:{res:-10}}{col 53}{ralign 17:Left-censored}{col 70} = {res}{ralign 6:1}
{txt}{col 1}        Upper = {ralign 3:{res:10}}{col 53}{ralign 17:   Right-censored}{col 70} = {res}{ralign 6:1,100}

{txt}{col 53}{lalign 17:LR chi2({res:28})}{col 70} = {res}{ralign 6:140.20}
{txt}{col 53}{lalign 17:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-3033.8071}{txt}{col 53}{lalign 17:Pseudo R2}{col 70} = {res}{ralign 6:0.0226}

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.7570838{col 41}{space 2} .3351415{col 52}{space 1}   -2.26{col 61}{space 3}0.024{col 69}{space 4}-1.414378{col 82}{space 3}-.0997897
{txt}{space 12}addrvictimindex {c |}{col 29}{res}{space 2} -.700868{col 41}{space 2} .5666453{col 52}{space 1}   -1.24{col 61}{space 3}0.216{col 69}{space 4}-1.812198{col 82}{space 3} .4104616
{txt}{space 27} {c |}
{space 16}victimorder#{c |}
{space 10}c.addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .3872634{col 41}{space 2} .4797481{col 52}{space 1}    0.81{col 61}{space 3}0.420{col 69}{space 4}-.5536394{col 82}{space 3} 1.328166
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.3544262{col 41}{space 2}  .275279{col 52}{space 1}   -1.29{col 61}{space 3}0.198{col 69}{space 4}-.8943153{col 82}{space 3}  .185463
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2}  1.15392{col 41}{space 2} 1.971082{col 52}{space 1}    0.59{col 61}{space 3}0.558{col 69}{space 4}-2.711852{col 82}{space 3} 5.019692
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .8363983{col 41}{space 2} .5905589{col 52}{space 1}    1.42{col 61}{space 3}0.157{col 69}{space 4}-.3218317{col 82}{space 3} 1.994628
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2} 1.467898{col 41}{space 2} 1.572314{col 52}{space 1}    0.93{col 61}{space 3}0.351{col 69}{space 4}-1.615793{col 82}{space 3} 4.551589
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .8281018{col 41}{space 2} .4696517{col 52}{space 1}    1.76{col 61}{space 3}0.078{col 69}{space 4}-.0929996{col 82}{space 3} 1.749203
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .7878384{col 41}{space 2} .8154331{col 52}{space 1}    0.97{col 61}{space 3}0.334{col 69}{space 4}-.8114246{col 82}{space 3} 2.387101
{txt}{space 21}female {c |}{col 29}{res}{space 2} 1.019138{col 41}{space 2} .2942639{col 52}{space 1}    3.46{col 61}{space 3}0.001{col 69}{space 4} .4420153{col 82}{space 3} 1.596262
{txt}{space 24}age {c |}{col 29}{res}{space 2} .0065002{col 41}{space 2} .0123233{col 52}{space 1}    0.53{col 61}{space 3}0.598{col 69}{space 4}-.0176688{col 82}{space 3} .0306693
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.1768574{col 41}{space 2} .0977678{col 52}{space 1}   -1.81{col 61}{space 3}0.071{col 69}{space 4}-.3686039{col 82}{space 3} .0148892
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-3.184061{col 41}{space 2} .8354439{col 52}{space 1}   -3.81{col 61}{space 3}0.000{col 69}{space 4} -4.82257{col 82}{space 3}-1.545552
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-1.671676{col 41}{space 2} .3699312{col 52}{space 1}   -4.52{col 61}{space 3}0.000{col 69}{space 4}-2.397201{col 82}{space 3} -.946151
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .2379648{col 41}{space 2} .6043288{col 52}{space 1}    0.39{col 61}{space 3}0.694{col 69}{space 4}-.9472712{col 82}{space 3} 1.423201
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2}-.0419931{col 41}{space 2} .4997199{col 52}{space 1}   -0.08{col 61}{space 3}0.933{col 69}{space 4}-1.022065{col 82}{space 3} .9380794
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2}-.3808289{col 41}{space 2} .6850639{col 52}{space 1}   -0.56{col 61}{space 3}0.578{col 69}{space 4}-1.724406{col 82}{space 3} .9627483
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .3966262{col 41}{space 2} .7062742{col 52}{space 1}    0.56{col 61}{space 3}0.574{col 69}{space 4}-.9885495{col 82}{space 3} 1.781802
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} 2.385843{col 41}{space 2} 1.086935{col 52}{space 1}    2.20{col 61}{space 3}0.028{col 69}{space 4} .2540989{col 82}{space 3} 4.517587
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.3063682{col 41}{space 2} .6543431{col 52}{space 1}   -0.47{col 61}{space 3}0.640{col 69}{space 4}-1.589695{col 82}{space 3} .9769581
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .2934783{col 41}{space 2} .5330463{col 52}{space 1}    0.55{col 61}{space 3}0.582{col 69}{space 4}-.7519553{col 82}{space 3} 1.338912
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-.2334881{col 41}{space 2} .8808751{col 52}{space 1}   -0.27{col 61}{space 3}0.791{col 69}{space 4}-1.961099{col 82}{space 3} 1.494123
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.8805023{col 41}{space 2} .6024543{col 52}{space 1}   -1.46{col 61}{space 3}0.144{col 69}{space 4}-2.062062{col 82}{space 3} .3010573
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2}  .209246{col 41}{space 2} .1244967{col 52}{space 1}    1.68{col 61}{space 3}0.093{col 69}{space 4}-.0349224{col 82}{space 3} .4534144
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .4648445{col 41}{space 2}   .36791{col 52}{space 1}    1.26{col 61}{space 3}0.207{col 69}{space 4}-.2567166{col 82}{space 3} 1.186406
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.3350002{col 41}{space 2} .3927681{col 52}{space 1}   -0.85{col 61}{space 3}0.394{col 69}{space 4}-1.105314{col 82}{space 3} .4353138
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-.3668851{col 41}{space 2} .4514383{col 52}{space 1}   -0.81{col 61}{space 3}0.416{col 69}{space 4}-1.252266{col 82}{space 3} .5184955
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-2.194601{col 41}{space 2}   .54917{col 52}{space 1}   -4.00{col 61}{space 3}0.000{col 69}{space 4}-3.271657{col 82}{space 3}-1.117545
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 11.67264{col 41}{space 2} 1.218542{col 52}{space 1}    9.58{col 61}{space 3}0.000{col 69}{space 4} 9.282782{col 82}{space 3}  14.0625
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}var(e.magcitizenbias){c |}{col 29}{res}{space 2} 26.24815{col 41}{space 2} 1.502692{col 69}{space 4} 23.46044{col 82}{space 3} 29.36712
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Robustness Check: Binary Victimization Measurement
. 
. reg magukbias victimorder##drvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,800
                                                {txt}F(28, 1771)       =  {res}     8.02
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0911
                                                {txt}Root MSE          =    {res} 3.7296

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.4972259{col 41}{space 2} .2310692{col 52}{space 1}   -2.15{col 61}{space 3}0.032{col 69}{space 4}-.9504231{col 82}{space 3}-.0440288
{txt}{space 12}1.drvictimindex {c |}{col 29}{res}{space 2}-.5655758{col 41}{space 2} .3856192{col 52}{space 1}   -1.47{col 61}{space 3}0.143{col 69}{space 4}-1.321892{col 82}{space 3} .1907408
{txt}{space 27} {c |}
{space 2}victimorder#drvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .3425177{col 41}{space 2} .3626966{col 52}{space 1}    0.94{col 61}{space 3}0.345{col 69}{space 4}-.3688408{col 82}{space 3} 1.053876
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.2529465{col 41}{space 2} .1777196{col 52}{space 1}   -1.42{col 61}{space 3}0.155{col 69}{space 4}-.6015086{col 82}{space 3} .0956157
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2}-.0729977{col 41}{space 2} .9329134{col 52}{space 1}   -0.08{col 61}{space 3}0.938{col 69}{space 4}-1.902725{col 82}{space 3} 1.756729
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .5814368{col 41}{space 2} .3465384{col 52}{space 1}    1.68{col 61}{space 3}0.094{col 69}{space 4}-.0982305{col 82}{space 3} 1.261104
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2} .1119161{col 41}{space 2} .7046819{col 52}{space 1}    0.16{col 61}{space 3}0.874{col 69}{space 4} -1.27018{col 82}{space 3} 1.494012
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .1945883{col 41}{space 2} .2901774{col 52}{space 1}    0.67{col 61}{space 3}0.503{col 69}{space 4}-.3745379{col 82}{space 3} .7637145
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .5846711{col 41}{space 2} .4830458{col 52}{space 1}    1.21{col 61}{space 3}0.226{col 69}{space 4}-.3627287{col 82}{space 3} 1.532071
{txt}{space 21}female {c |}{col 29}{res}{space 2}  .525492{col 41}{space 2} .1872029{col 52}{space 1}    2.81{col 61}{space 3}0.005{col 69}{space 4}   .15833{col 82}{space 3} .8926539
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0348511{col 41}{space 2} .0081279{col 52}{space 1}   -4.29{col 61}{space 3}0.000{col 69}{space 4}-.0507923{col 82}{space 3}-.0189098
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0466014{col 41}{space 2} .0642108{col 52}{space 1}   -0.73{col 61}{space 3}0.468{col 69}{space 4}-.1725383{col 82}{space 3} .0793356
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-2.228328{col 41}{space 2} .5274286{col 52}{space 1}   -4.22{col 61}{space 3}0.000{col 69}{space 4}-3.262776{col 82}{space 3} -1.19388
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-1.023298{col 41}{space 2} .2629169{col 52}{space 1}   -3.89{col 61}{space 3}0.000{col 69}{space 4}-1.538959{col 82}{space 3}-.5076383
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2}-.1163574{col 41}{space 2}  .383782{col 52}{space 1}   -0.30{col 61}{space 3}0.762{col 69}{space 4}-.8690706{col 82}{space 3} .6363558
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2}  .141914{col 41}{space 2}  .316703{col 52}{space 1}    0.45{col 61}{space 3}0.654{col 69}{space 4} -.479237{col 82}{space 3} .7630649
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} -.935183{col 41}{space 2} .4346258{col 52}{space 1}   -2.15{col 61}{space 3}0.032{col 69}{space 4}-1.787617{col 82}{space 3}-.0827494
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2}-.1593234{col 41}{space 2}  .426533{col 52}{space 1}   -0.37{col 61}{space 3}0.709{col 69}{space 4}-.9958845{col 82}{space 3} .6772377
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2}-.0948268{col 41}{space 2}  .566897{col 52}{space 1}   -0.17{col 61}{space 3}0.867{col 69}{space 4}-1.206684{col 82}{space 3} 1.017031
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0205971{col 41}{space 2} .4287881{col 52}{space 1}   -0.05{col 61}{space 3}0.962{col 69}{space 4}-.8615812{col 82}{space 3} .8203869
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2}  .293996{col 41}{space 2} .3513461{col 52}{space 1}    0.84{col 61}{space 3}0.403{col 69}{space 4}-.3951007{col 82}{space 3} .9830927
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-1.241021{col 41}{space 2} .5114952{col 52}{space 1}   -2.43{col 61}{space 3}0.015{col 69}{space 4}-2.244219{col 82}{space 3}-.2378236
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.8654898{col 41}{space 2} .4042526{col 52}{space 1}   -2.14{col 61}{space 3}0.032{col 69}{space 4}-1.658352{col 82}{space 3}-.0726273
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .1209316{col 41}{space 2} .0852106{col 52}{space 1}    1.42{col 61}{space 3}0.156{col 69}{space 4}-.0461924{col 82}{space 3} .2880556
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .2543758{col 41}{space 2} .2310414{col 52}{space 1}    1.10{col 61}{space 3}0.271{col 69}{space 4}-.1987667{col 82}{space 3} .7075184
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2} -.811233{col 41}{space 2} .2321825{col 52}{space 1}   -3.49{col 61}{space 3}0.000{col 69}{space 4}-1.266614{col 82}{space 3}-.3558524
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-1.045551{col 41}{space 2} .2791028{col 52}{space 1}   -3.75{col 61}{space 3}0.000{col 69}{space 4}-1.592957{col 82}{space 3}-.4981459
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.796381{col 41}{space 2}  .359771{col 52}{space 1}   -4.99{col 61}{space 3}0.000{col 69}{space 4}-2.502001{col 82}{space 3} -1.09076
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 7.685816{col 41}{space 2} .8062734{col 52}{space 1}    9.53{col 61}{space 3}0.000{col 69}{space 4} 6.104468{col 82}{space 3} 9.267164
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg magcitizenbias victimorder##drvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,883
                                                {txt}F(28, 1854)       =  {res}     4.23
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0771
                                                {txt}Root MSE          =    {res} 2.6493

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.4042199{col 41}{space 2} .1575794{col 52}{space 1}   -2.57{col 61}{space 3}0.010{col 69}{space 4}-.7132717{col 82}{space 3}-.0951682
{txt}{space 12}1.drvictimindex {c |}{col 29}{res}{space 2} -.165876{col 41}{space 2}  .286102{col 52}{space 1}   -0.58{col 61}{space 3}0.562{col 69}{space 4} -.726992{col 82}{space 3}   .39524
{txt}{space 27} {c |}
{space 2}victimorder#drvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .3193603{col 41}{space 2} .2538331{col 52}{space 1}    1.26{col 61}{space 3}0.208{col 69}{space 4}-.1784684{col 82}{space 3}  .817189
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.0791843{col 41}{space 2} .1224639{col 52}{space 1}   -0.65{col 61}{space 3}0.518{col 69}{space 4} -.319366{col 82}{space 3} .1609974
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2} .3044473{col 41}{space 2} .6525508{col 52}{space 1}    0.47{col 61}{space 3}0.641{col 69}{space 4}-.9753642{col 82}{space 3} 1.584259
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .0966615{col 41}{space 2}  .260699{col 52}{space 1}    0.37{col 61}{space 3}0.711{col 69}{space 4}-.4146329{col 82}{space 3} .6079558
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2}-.1617325{col 41}{space 2} .8178056{col 52}{space 1}   -0.20{col 61}{space 3}0.843{col 69}{space 4}-1.765649{col 82}{space 3} 1.442184
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .4376091{col 41}{space 2} .1960833{col 52}{space 1}    2.23{col 61}{space 3}0.026{col 69}{space 4} .0530418{col 82}{space 3} .8221764
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .4971534{col 41}{space 2}  .336807{col 52}{space 1}    1.48{col 61}{space 3}0.140{col 69}{space 4}-.1634074{col 82}{space 3} 1.157714
{txt}{space 21}female {c |}{col 29}{res}{space 2} .3640286{col 41}{space 2} .1400137{col 52}{space 1}    2.60{col 61}{space 3}0.009{col 69}{space 4} .0894275{col 82}{space 3} .6386297
{txt}{space 24}age {c |}{col 29}{res}{space 2} .0003579{col 41}{space 2} .0054467{col 52}{space 1}    0.07{col 61}{space 3}0.948{col 69}{space 4}-.0103244{col 82}{space 3} .0110402
{txt}{space 18}education {c |}{col 29}{res}{space 2} -.033703{col 41}{space 2} .0452304{col 52}{space 1}   -0.75{col 61}{space 3}0.456{col 69}{space 4}-.1224108{col 82}{space 3} .0550048
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-1.669614{col 41}{space 2} .5467124{col 52}{space 1}   -3.05{col 61}{space 3}0.002{col 69}{space 4}-2.741851{col 82}{space 3}-.5973779
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-.9367266{col 41}{space 2} .2057541{col 52}{space 1}   -4.55{col 61}{space 3}0.000{col 69}{space 4}-1.340261{col 82}{space 3}-.5331925
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .0751404{col 41}{space 2}  .271642{col 52}{space 1}    0.28{col 61}{space 3}0.782{col 69}{space 4} -.457616{col 82}{space 3} .6078968
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .0467566{col 41}{space 2} .2350632{col 52}{space 1}    0.20{col 61}{space 3}0.842{col 69}{space 4}-.4142598{col 82}{space 3} .5077729
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .1008655{col 41}{space 2} .3014871{col 52}{space 1}    0.33{col 61}{space 3}0.738{col 69}{space 4}-.4904244{col 82}{space 3} .6921554
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .2293558{col 41}{space 2} .2965558{col 52}{space 1}    0.77{col 61}{space 3}0.439{col 69}{space 4}-.3522625{col 82}{space 3} .8109741
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .7832266{col 41}{space 2} .3496779{col 52}{space 1}    2.24{col 61}{space 3}0.025{col 69}{space 4} .0974228{col 82}{space 3}  1.46903
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0845201{col 41}{space 2} .3155952{col 52}{space 1}   -0.27{col 61}{space 3}0.789{col 69}{space 4}-.7034794{col 82}{space 3} .5344392
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .1313153{col 41}{space 2} .2463182{col 52}{space 1}    0.53{col 61}{space 3}0.594{col 69}{space 4}-.3517749{col 82}{space 3} .6144055
{txt}{space 19}Student  {c |}{col 29}{res}{space 2} .0741223{col 41}{space 2} .3718142{col 52}{space 1}    0.20{col 61}{space 3}0.842{col 69}{space 4}-.6550962{col 82}{space 3} .8033408
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.3998274{col 41}{space 2} .3070951{col 52}{space 1}   -1.30{col 61}{space 3}0.193{col 69}{space 4}-1.002116{col 82}{space 3}  .202461
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .0895007{col 41}{space 2} .0607716{col 52}{space 1}    1.47{col 61}{space 3}0.141{col 69}{space 4}-.0296873{col 82}{space 3} .2086887
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .2087269{col 41}{space 2} .1522557{col 52}{space 1}    1.37{col 61}{space 3}0.171{col 69}{space 4}-.0898836{col 82}{space 3} .5073374
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.0787633{col 41}{space 2} .1587711{col 52}{space 1}   -0.50{col 61}{space 3}0.620{col 69}{space 4}-.3901522{col 82}{space 3} .2326257
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-.1937323{col 41}{space 2} .1977675{col 52}{space 1}   -0.98{col 61}{space 3}0.327{col 69}{space 4}-.5816026{col 82}{space 3}  .194138
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.092714{col 41}{space 2} .2628912{col 52}{space 1}   -4.16{col 61}{space 3}0.000{col 69}{space 4}-1.608308{col 82}{space 3}-.5771202
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.400976{col 41}{space 2} .5531053{col 52}{space 1}   15.19{col 61}{space 3}0.000{col 69}{space 4} 7.316201{col 82}{space 3}  9.48575
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Robustness Check – Ordinal Measure of Victimization
. 
. tab addrvictimindex

{txt}addrvictimi {c |}
       ndex {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,274       63.70       63.70
{txt}          1 {c |}{res}        645       32.25       95.95
{txt}          2 {c |}{res}         74        3.70       99.65
{txt}          3 {c |}{res}          7        0.35      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,000      100.00
{txt}
{com}. reg magukbias victimorder##i.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,800
                                                {txt}F(32, 1767)       =  {res}     7.05
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0916
                                                {txt}Root MSE          =    {res} 3.7328

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2} -.497099{col 41}{space 2} .2313307{col 52}{space 1}   -2.15{col 61}{space 3}0.032{col 69}{space 4}-.9508097{col 82}{space 3}-.0433883
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2}-.5076417{col 41}{space 2} .3903149{col 52}{space 1}   -1.30{col 61}{space 3}0.194{col 69}{space 4}-1.273169{col 82}{space 3} .2578858
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.4580788{col 41}{space 2} .8413391{col 52}{space 1}   -0.54{col 61}{space 3}0.586{col 69}{space 4}-2.108203{col 82}{space 3} 1.192046
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-1.936213{col 41}{space 2} 2.893342{col 52}{space 1}   -0.67{col 61}{space 3}0.503{col 69}{space 4}-7.610945{col 82}{space 3}  3.73852
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .2650851{col 41}{space 2} .3732391{col 52}{space 1}    0.71{col 61}{space 3}0.478{col 69}{space 4}-.4669515{col 82}{space 3} .9971217
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2} .7058573{col 41}{space 2} .9688807{col 52}{space 1}    0.73{col 61}{space 3}0.466{col 69}{space 4}-1.194416{col 82}{space 3}  2.60613
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2} 2.764118{col 41}{space 2} 3.528097{col 52}{space 1}    0.78{col 61}{space 3}0.433{col 69}{space 4}-4.155565{col 82}{space 3}   9.6838
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.2586017{col 41}{space 2} .1779086{col 52}{space 1}   -1.45{col 61}{space 3}0.146{col 69}{space 4}-.6075352{col 82}{space 3} .0903319
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2}-.3679908{col 41}{space 2} .9732551{col 52}{space 1}   -0.38{col 61}{space 3}0.705{col 69}{space 4}-2.276843{col 82}{space 3} 1.540862
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .5285545{col 41}{space 2} .3584143{col 52}{space 1}    1.47{col 61}{space 3}0.140{col 69}{space 4}-.1744061{col 82}{space 3} 1.231515
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2} .0335249{col 41}{space 2} .7636089{col 52}{space 1}    0.04{col 61}{space 3}0.965{col 69}{space 4}-1.464147{col 82}{space 3} 1.531197
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .1895799{col 41}{space 2} .2905544{col 52}{space 1}    0.65{col 61}{space 3}0.514{col 69}{space 4}-.3802866{col 82}{space 3} .7594465
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .5750361{col 41}{space 2} .4907094{col 52}{space 1}    1.17{col 61}{space 3}0.241{col 69}{space 4}-.3873958{col 82}{space 3} 1.537468
{txt}{space 21}female {c |}{col 29}{res}{space 2} .5267326{col 41}{space 2}  .187442{col 52}{space 1}    2.81{col 61}{space 3}0.005{col 69}{space 4} .1591012{col 82}{space 3} .8943641
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0350935{col 41}{space 2} .0081391{col 52}{space 1}   -4.31{col 61}{space 3}0.000{col 69}{space 4}-.0510566{col 82}{space 3}-.0191303
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0441633{col 41}{space 2} .0642836{col 52}{space 1}   -0.69{col 61}{space 3}0.492{col 69}{space 4}-.1702432{col 82}{space 3} .0819165
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-2.231632{col 41}{space 2} .5280486{col 52}{space 1}   -4.23{col 61}{space 3}0.000{col 69}{space 4}-3.267298{col 82}{space 3}-1.195967
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-1.011561{col 41}{space 2} .2637708{col 52}{space 1}   -3.83{col 61}{space 3}0.000{col 69}{space 4}-1.528897{col 82}{space 3}-.4942254
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} -.127081{col 41}{space 2} .3865231{col 52}{space 1}   -0.33{col 61}{space 3}0.742{col 69}{space 4}-.8851716{col 82}{space 3} .6310097
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .1268643{col 41}{space 2} .3177204{col 52}{space 1}    0.40{col 61}{space 3}0.690{col 69}{space 4}-.4962831{col 82}{space 3} .7500116
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} -.945247{col 41}{space 2} .4328584{col 52}{space 1}   -2.18{col 61}{space 3}0.029{col 69}{space 4}-1.794215{col 82}{space 3}-.0962785
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2}-.1644436{col 41}{space 2} .4267271{col 52}{space 1}   -0.39{col 61}{space 3}0.700{col 69}{space 4}-1.001387{col 82}{space 3} .6724994
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2}-.0833474{col 41}{space 2} .5671785{col 52}{space 1}   -0.15{col 61}{space 3}0.883{col 69}{space 4}-1.195759{col 82}{space 3} 1.029064
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0312785{col 41}{space 2} .4298438{col 52}{space 1}   -0.07{col 61}{space 3}0.942{col 69}{space 4}-.8743344{col 82}{space 3} .8117774
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .2996903{col 41}{space 2} .3521532{col 52}{space 1}    0.85{col 61}{space 3}0.395{col 69}{space 4}-.3909905{col 82}{space 3} .9903711
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-1.239263{col 41}{space 2} .5127006{col 52}{space 1}   -2.42{col 61}{space 3}0.016{col 69}{space 4}-2.244826{col 82}{space 3} -.233699
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2} -.873431{col 41}{space 2} .4050277{col 52}{space 1}   -2.16{col 61}{space 3}0.031{col 69}{space 4}-1.667815{col 82}{space 3}-.0790471
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .1212739{col 41}{space 2} .0852664{col 52}{space 1}    1.42{col 61}{space 3}0.155{col 69}{space 4}-.0459598{col 82}{space 3} .2885076
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .2528963{col 41}{space 2} .2316526{col 52}{space 1}    1.09{col 61}{space 3}0.275{col 69}{space 4}-.2014456{col 82}{space 3} .7072382
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.8186767{col 41}{space 2} .2328618{col 52}{space 1}   -3.52{col 61}{space 3}0.000{col 69}{space 4} -1.27539{col 82}{space 3}-.3619631
{txt}{space 21}South  {c |}{col 29}{res}{space 2} -1.05322{col 41}{space 2} .2791918{col 52}{space 1}   -3.77{col 61}{space 3}0.000{col 69}{space 4}-1.600801{col 82}{space 3} -.505639
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.827979{col 41}{space 2} .3608088{col 52}{space 1}   -5.07{col 61}{space 3}0.000{col 69}{space 4}-2.535636{col 82}{space 3}-1.120322
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 7.702457{col 41}{space 2} .8077912{col 52}{space 1}    9.54{col 61}{space 3}0.000{col 69}{space 4}  6.11813{col 82}{space 3} 9.286784
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg magcitizenbias victimorder##i.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,883
                                                {txt}F(32, 1850)       =  {res}     3.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0794
                                                {txt}Root MSE          =    {res} 2.6488

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.4014248{col 41}{space 2} .1577902{col 52}{space 1}   -2.54{col 61}{space 3}0.011{col 69}{space 4}-.7108905{col 82}{space 3}-.0919592
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} -.220543{col 41}{space 2}  .290326{col 52}{space 1}   -0.76{col 61}{space 3}0.448{col 69}{space 4} -.789944{col 82}{space 3}  .348858
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.2586226{col 41}{space 2} .7499347{col 52}{space 1}   -0.34{col 61}{space 3}0.730{col 69}{space 4} -1.72943{col 82}{space 3} 1.212185
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-4.208013{col 41}{space 2} 3.831392{col 52}{space 1}   -1.10{col 61}{space 3}0.272{col 69}{space 4}-11.72232{col 82}{space 3} 3.306294
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .3610784{col 41}{space 2} .2553198{col 52}{space 1}    1.41{col 61}{space 3}0.157{col 69}{space 4}-.1396667{col 82}{space 3} .8618236
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2}-.1092919{col 41}{space 2}   .86972{col 52}{space 1}   -0.13{col 61}{space 3}0.900{col 69}{space 4}-1.815028{col 82}{space 3} 1.596444
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2} 3.657752{col 41}{space 2} 3.828743{col 52}{space 1}    0.96{col 61}{space 3}0.340{col 69}{space 4}-3.851359{col 82}{space 3} 11.16686
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.0774439{col 41}{space 2} .1226205{col 52}{space 1}   -0.63{col 61}{space 3}0.528{col 69}{space 4}-.3179329{col 82}{space 3} .1630451
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2} .5758549{col 41}{space 2}  .774427{col 52}{space 1}    0.74{col 61}{space 3}0.457{col 69}{space 4}-.9429879{col 82}{space 3} 2.094698
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .1634811{col 41}{space 2} .2688941{col 52}{space 1}    0.61{col 61}{space 3}0.543{col 69}{space 4}-.3638867{col 82}{space 3}  .690849
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2} .3946148{col 41}{space 2} .9713931{col 52}{space 1}    0.41{col 61}{space 3}0.685{col 69}{space 4}-1.510527{col 82}{space 3} 2.299757
{txt}{space 22}moved {c |}{col 29}{res}{space 2}  .472323{col 41}{space 2} .1991726{col 52}{space 1}    2.37{col 61}{space 3}0.018{col 69}{space 4} .0816963{col 82}{space 3} .8629498
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .4900169{col 41}{space 2} .3473453{col 52}{space 1}    1.41{col 61}{space 3}0.158{col 69}{space 4} -.191213{col 82}{space 3} 1.171247
{txt}{space 21}female {c |}{col 29}{res}{space 2} .3707723{col 41}{space 2} .1398356{col 52}{space 1}    2.65{col 61}{space 3}0.008{col 69}{space 4} .0965201{col 82}{space 3} .6450244
{txt}{space 24}age {c |}{col 29}{res}{space 2} .0001214{col 41}{space 2}  .005451{col 52}{space 1}    0.02{col 61}{space 3}0.982{col 69}{space 4}-.0105694{col 82}{space 3} .0108123
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0309166{col 41}{space 2} .0450611{col 52}{space 1}   -0.69{col 61}{space 3}0.493{col 69}{space 4}-.1192925{col 82}{space 3} .0574593
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-1.670938{col 41}{space 2} .5475693{col 52}{space 1}   -3.05{col 61}{space 3}0.002{col 69}{space 4}-2.744857{col 82}{space 3}-.5970193
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-.9344383{col 41}{space 2} .2044753{col 52}{space 1}   -4.57{col 61}{space 3}0.000{col 69}{space 4}-1.335465{col 82}{space 3}-.5334117
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .1115863{col 41}{space 2}  .269983{col 52}{space 1}    0.41{col 61}{space 3}0.679{col 69}{space 4}-.4179171{col 82}{space 3} .6410896
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .0425523{col 41}{space 2} .2353589{col 52}{space 1}    0.18{col 61}{space 3}0.857{col 69}{space 4}-.4190446{col 82}{space 3} .5041492
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .0955599{col 41}{space 2} .3024811{col 52}{space 1}    0.32{col 61}{space 3}0.752{col 69}{space 4}-.4976803{col 82}{space 3} .6888001
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .2335606{col 41}{space 2} .2977656{col 52}{space 1}    0.78{col 61}{space 3}0.433{col 69}{space 4}-.3504313{col 82}{space 3} .8175525
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .7701866{col 41}{space 2} .3510941{col 52}{space 1}    2.19{col 61}{space 3}0.028{col 69}{space 4} .0816043{col 82}{space 3} 1.458769
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0862988{col 41}{space 2} .3157719{col 52}{space 1}   -0.27{col 61}{space 3}0.785{col 69}{space 4}-.7056055{col 82}{space 3} .5330078
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .1417277{col 41}{space 2} .2463161{col 52}{space 1}    0.58{col 61}{space 3}0.565{col 69}{space 4} -.341359{col 82}{space 3} .6248144
{txt}{space 19}Student  {c |}{col 29}{res}{space 2} .0615203{col 41}{space 2} .3725208{col 52}{space 1}    0.17{col 61}{space 3}0.869{col 69}{space 4}-.6690851{col 82}{space 3} .7921257
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.4081322{col 41}{space 2} .3054963{col 52}{space 1}   -1.34{col 61}{space 3}0.182{col 69}{space 4}-1.007286{col 82}{space 3} .1910216
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .0869343{col 41}{space 2} .0608523{col 52}{space 1}    1.43{col 61}{space 3}0.153{col 69}{space 4}-.0324121{col 82}{space 3} .2062806
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .2060088{col 41}{space 2}  .152631{col 52}{space 1}    1.35{col 61}{space 3}0.177{col 69}{space 4}-.0933384{col 82}{space 3} .5053559
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.0606501{col 41}{space 2} .1587881{col 52}{space 1}   -0.38{col 61}{space 3}0.703{col 69}{space 4}-.3720727{col 82}{space 3} .2507726
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-.1885911{col 41}{space 2} .1967624{col 52}{space 1}   -0.96{col 61}{space 3}0.338{col 69}{space 4}-.5744909{col 82}{space 3} .1973087
{txt}{space 22}East  {c |}{col 29}{res}{space 2} -1.07018{col 41}{space 2} .2645326{col 52}{space 1}   -4.05{col 61}{space 3}0.000{col 69}{space 4}-1.588994{col 82}{space 3}-.5513662
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.379067{col 41}{space 2} .5530514{col 52}{space 1}   15.15{col 61}{space 3}0.000{col 69}{space 4} 7.294397{col 82}{space 3} 9.463738
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Moderated Effects of Victimization Experience on Social Distance (OLS Regression)
. 
. reg magukbias victimorder##i.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,832
                                                {txt}F(7, 1824)        =  {res}     1.27
                                                {txt}Prob > F          = {res}    0.2596
                                                {txt}R-squared         = {res}    0.0046
                                                {txt}Root MSE          =    {res} 3.8739

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.4287272{col 41}{space 2} .2352291{col 52}{space 1}   -1.82{col 61}{space 3}0.069{col 69}{space 4}-.8900739{col 82}{space 3} .0326196
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .2601223{col 41}{space 2} .2638236{col 52}{space 1}    0.99{col 61}{space 3}0.324{col 69}{space 4}-.2573057{col 82}{space 3} .7775504
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.1195058{col 41}{space 2}  .668843{col 52}{space 1}   -0.18{col 61}{space 3}0.858{col 69}{space 4}-1.431284{col 82}{space 3} 1.192273
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-1.426523{col 41}{space 2}    1.917{col 52}{space 1}   -0.74{col 61}{space 3}0.457{col 69}{space 4}-5.186269{col 82}{space 3} 2.333222
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .2187972{col 41}{space 2} .3789719{col 52}{space 1}    0.58{col 61}{space 3}0.564{col 69}{space 4}-.5244672{col 82}{space 3} .9620616
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2} .6836144{col 41}{space 2} .9657294{col 52}{space 1}    0.71{col 61}{space 3}0.479{col 69}{space 4}-1.210437{col 82}{space 3} 2.577666
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2}  2.51206{col 41}{space 2} 2.551475{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4}-2.492059{col 82}{space 3}  7.51618
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}  5.09319{col 41}{space 2} .1712857{col 52}{space 1}   29.74{col 61}{space 3}0.000{col 69}{space 4} 4.757253{col 82}{space 3} 5.429127
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins victimorder, at(addrvictimindex=(0 (1) 3))
{res}
{txt}{col 1}Adjusted predictions{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,832}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:3}}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}     Margin{col 29}   std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_at#victimorder {c |}
{space 11}1 0  {c |}{col 17}{res}{space 2}  5.09319{col 29}{space 2} .1712857{col 40}{space 1}   29.74{col 49}{space 3}0.000{col 57}{space 4} 4.757253{col 70}{space 3} 5.429127
{txt}{space 11}1 1  {c |}{col 17}{res}{space 2} 4.664463{col 29}{space 2} .1612264{col 40}{space 1}   28.93{col 49}{space 3}0.000{col 57}{space 4} 4.348255{col 70}{space 3} 4.980671
{txt}{space 11}2 0  {c |}{col 17}{res}{space 2} 5.353312{col 29}{space 2} .2006591{col 40}{space 1}   26.68{col 49}{space 3}0.000{col 57}{space 4} 4.959767{col 70}{space 3} 5.746858
{txt}{space 11}2 1  {c |}{col 17}{res}{space 2} 5.143382{col 29}{space 2} .2191412{col 40}{space 1}   23.47{col 49}{space 3}0.000{col 57}{space 4} 4.713588{col 70}{space 3} 5.573176
{txt}{space 11}3 0  {c |}{col 17}{res}{space 2} 4.973684{col 29}{space 2} .6465386{col 40}{space 1}    7.69{col 49}{space 3}0.000{col 57}{space 4}  3.70565{col 70}{space 3} 6.241718
{txt}{space 11}3 1  {c |}{col 17}{res}{space 2} 5.228571{col 29}{space 2} .6777082{col 40}{space 1}    7.72{col 49}{space 3}0.000{col 57}{space 4} 3.899406{col 70}{space 3} 6.557737
{txt}{space 11}4 0  {c |}{col 17}{res}{space 2} 3.666667{col 29}{space 2} 1.909332{col 40}{space 1}    1.92{col 49}{space 3}0.055{col 57}{space 4}-.0780404{col 70}{space 3} 7.411374
{txt}{space 11}4 1  {c |}{col 17}{res}{space 2}     5.75{col 29}{space 2}  1.67605{col 40}{space 1}    3.43{col 49}{space 3}0.001{col 57}{space 4} 2.462822{col 70}{space 3} 9.037178
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:addrvictimindex victimorder}{p_end}
{res}{txt}
{com}. graph save g5
{res}{txt}file {bf:g5.gph} saved

{com}. reg magcitizenbias victimorder##i.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,924
                                                {txt}F(7, 1916)        =  {res}     1.62
                                                {txt}Prob > F          = {res}    0.1254
                                                {txt}R-squared         = {res}    0.0073
                                                {txt}Root MSE          =    {res} 2.7294

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.3227291{col 41}{space 2} .1604647{col 52}{space 1}   -2.01{col 61}{space 3}0.044{col 69}{space 4} -.637433{col 82}{space 3}-.0080253
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .1612591{col 41}{space 2} .1728239{col 52}{space 1}    0.93{col 61}{space 3}0.351{col 69}{space 4}-.1776837{col 82}{space 3} .5002019
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.0858452{col 41}{space 2} .5417101{col 52}{space 1}   -0.16{col 61}{space 3}0.874{col 69}{space 4}-1.148249{col 82}{space 3} .9765582
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-3.410169{col 41}{space 2} 2.364414{col 52}{space 1}   -1.44{col 61}{space 3}0.149{col 69}{space 4}-8.047266{col 82}{space 3} 1.226927
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .2530306{col 41}{space 2} .2570289{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4}-.2510551{col 82}{space 3} .7571164
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2} .0539603{col 41}{space 2} .7555469{col 52}{space 1}    0.07{col 61}{space 3}0.943{col 69}{space 4} -1.42782{col 82}{space 3} 1.535741
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2} 3.322729{col 41}{space 2} 2.666602{col 52}{space 1}    1.25{col 61}{space 3}0.213{col 69}{space 4}-1.907018{col 82}{space 3} 8.552476
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.410169{col 41}{space 2} .1081786{col 52}{space 1}   77.74{col 61}{space 3}0.000{col 69}{space 4} 8.198009{col 82}{space 3}  8.62233
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins victimorder, at(addrvictimindex=(0 (1) 3))
{res}
{txt}{col 1}Adjusted predictions{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,924}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:3}}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}     Margin{col 29}   std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_at#victimorder {c |}
{space 11}1 0  {c |}{col 17}{res}{space 2} 8.410169{col 29}{space 2} .1081786{col 40}{space 1}   77.74{col 49}{space 3}0.000{col 57}{space 4} 8.198009{col 70}{space 3}  8.62233
{txt}{space 11}1 1  {c |}{col 17}{res}{space 2}  8.08744{col 29}{space 2} .1185171{col 40}{space 1}   68.24{col 49}{space 3}0.000{col 57}{space 4} 7.855004{col 70}{space 3} 8.319877
{txt}{space 11}2 0  {c |}{col 17}{res}{space 2} 8.571429{col 29}{space 2} .1347795{col 40}{space 1}   63.60{col 49}{space 3}0.000{col 57}{space 4} 8.307099{col 70}{space 3} 8.835758
{txt}{space 11}2 1  {c |}{col 17}{res}{space 2}  8.50173{col 29}{space 2} .1488268{col 40}{space 1}   57.12{col 49}{space 3}0.000{col 57}{space 4} 8.209851{col 70}{space 3}  8.79361
{txt}{space 11}3 0  {c |}{col 17}{res}{space 2} 8.324324{col 29}{space 2} .5307986{col 40}{space 1}   15.68{col 49}{space 3}0.000{col 57}{space 4} 7.283321{col 70}{space 3} 9.365328
{txt}{space 11}3 1  {c |}{col 17}{res}{space 2} 8.055556{col 29}{space 2} .5131812{col 40}{space 1}   15.70{col 49}{space 3}0.000{col 57}{space 4} 7.049103{col 70}{space 3} 9.062008
{txt}{space 11}4 0  {c |}{col 17}{res}{space 2}        5{col 29}{space 2} 2.361938{col 40}{space 1}    2.12{col 49}{space 3}0.034{col 57}{space 4}   .36776{col 70}{space 3}  9.63224
{txt}{space 11}4 1  {c |}{col 17}{res}{space 2}        8{col 29}{space 2} 1.227299{col 40}{space 1}    6.52{col 49}{space 3}0.000{col 57}{space 4} 5.593017{col 70}{space 3} 10.40698
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:addrvictimindex victimorder}{p_end}
{res}{txt}
{com}. graph save g6
{res}{txt}file {bf:g6.gph} saved

{com}. graph combine "g5.gph" "g6.gph"
{res}{txt}
{com}. 
. reg magukbias victimorder##i.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,832
                                                {txt}F(7, 1824)        =  {res}     1.27
                                                {txt}Prob > F          = {res}    0.2596
                                                {txt}R-squared         = {res}    0.0046
                                                {txt}Root MSE          =    {res} 3.8739

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.4287272{col 41}{space 2} .2352291{col 52}{space 1}   -1.82{col 61}{space 3}0.069{col 69}{space 4}-.8900739{col 82}{space 3} .0326196
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .2601223{col 41}{space 2} .2638236{col 52}{space 1}    0.99{col 61}{space 3}0.324{col 69}{space 4}-.2573057{col 82}{space 3} .7775504
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.1195058{col 41}{space 2}  .668843{col 52}{space 1}   -0.18{col 61}{space 3}0.858{col 69}{space 4}-1.431284{col 82}{space 3} 1.192273
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-1.426523{col 41}{space 2}    1.917{col 52}{space 1}   -0.74{col 61}{space 3}0.457{col 69}{space 4}-5.186269{col 82}{space 3} 2.333222
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .2187972{col 41}{space 2} .3789719{col 52}{space 1}    0.58{col 61}{space 3}0.564{col 69}{space 4}-.5244672{col 82}{space 3} .9620616
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2} .6836144{col 41}{space 2} .9657294{col 52}{space 1}    0.71{col 61}{space 3}0.479{col 69}{space 4}-1.210437{col 82}{space 3} 2.577666
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2}  2.51206{col 41}{space 2} 2.551475{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4}-2.492059{col 82}{space 3}  7.51618
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}  5.09319{col 41}{space 2} .1712857{col 52}{space 1}   29.74{col 61}{space 3}0.000{col 69}{space 4} 4.757253{col 82}{space 3} 5.429127
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(victimorder) at(addrvictimindex=(0 (1) 3))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,832}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.victimorder}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:3}}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.victimorder {col 16}{txt}{c |}  (base outcome)
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.victimorder  {txt}{c |}
{space 11}_at {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.4287272{col 28}{space 2} .2352291{col 39}{space 1}   -1.82{col 48}{space 3}0.069{col 56}{space 4}-.8900739{col 69}{space 3} .0326196
{txt}{space 12}2  {c |}{col 16}{res}{space 2}-.2099299{col 28}{space 2} .2971312{col 39}{space 1}   -0.71{col 48}{space 3}0.480{col 56}{space 4}-.7926831{col 69}{space 3} .3728232
{txt}{space 12}3  {c |}{col 16}{res}{space 2} .2548872{col 28}{space 2} .9366432{col 39}{space 1}    0.27{col 48}{space 3}0.786{col 56}{space 4}-1.582119{col 69}{space 3} 2.091893
{txt}{space 12}4  {c |}{col 16}{res}{space 2} 2.083333{col 28}{space 2} 2.540608{col 39}{space 1}    0.82{col 48}{space 3}0.412{col 56}{space 4}-2.899474{col 69}{space 3} 7.066141
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:addrvictimindex}{p_end}
{res}{txt}
{com}. graph save g7
{res}{txt}file {bf:g7.gph} saved

{com}. reg magcitizenbias victimorder##i.addrvictimindex , robust

{txt}Linear regression                               Number of obs     = {res}     1,924
                                                {txt}F(7, 1916)        =  {res}     1.62
                                                {txt}Prob > F          = {res}    0.1254
                                                {txt}R-squared         = {res}    0.0073
                                                {txt}Root MSE          =    {res} 2.7294

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.3227291{col 41}{space 2} .1604647{col 52}{space 1}   -2.01{col 61}{space 3}0.044{col 69}{space 4} -.637433{col 82}{space 3}-.0080253
{txt}{space 27} {c |}
{space 12}addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .1612591{col 41}{space 2} .1728239{col 52}{space 1}    0.93{col 61}{space 3}0.351{col 69}{space 4}-.1776837{col 82}{space 3} .5002019
{txt}{space 25}2  {c |}{col 29}{res}{space 2}-.0858452{col 41}{space 2} .5417101{col 52}{space 1}   -0.16{col 61}{space 3}0.874{col 69}{space 4}-1.148249{col 82}{space 3} .9765582
{txt}{space 25}3  {c |}{col 29}{res}{space 2}-3.410169{col 41}{space 2} 2.364414{col 52}{space 1}   -1.44{col 61}{space 3}0.149{col 69}{space 4}-8.047266{col 82}{space 3} 1.226927
{txt}{space 27} {c |}
victimorder#addrvictimindex {c |}
{space 23}1 1  {c |}{col 29}{res}{space 2} .2530306{col 41}{space 2} .2570289{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4}-.2510551{col 82}{space 3} .7571164
{txt}{space 23}1 2  {c |}{col 29}{res}{space 2} .0539603{col 41}{space 2} .7555469{col 52}{space 1}    0.07{col 61}{space 3}0.943{col 69}{space 4} -1.42782{col 82}{space 3} 1.535741
{txt}{space 23}1 3  {c |}{col 29}{res}{space 2} 3.322729{col 41}{space 2} 2.666602{col 52}{space 1}    1.25{col 61}{space 3}0.213{col 69}{space 4}-1.907018{col 82}{space 3} 8.552476
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.410169{col 41}{space 2} .1081786{col 52}{space 1}   77.74{col 61}{space 3}0.000{col 69}{space 4} 8.198009{col 82}{space 3}  8.62233
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(victimorder) at(addrvictimindex=(0 (1) 3))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,924}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.victimorder}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 15:addrvictimindex} = {res:{ralign 1:3}}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.victimorder {col 16}{txt}{c |}  (base outcome)
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.victimorder  {txt}{c |}
{space 11}_at {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.3227291{col 28}{space 2} .1604647{col 39}{space 1}   -2.01{col 48}{space 3}0.044{col 56}{space 4} -.637433{col 69}{space 3}-.0080253
{txt}{space 12}2  {c |}{col 16}{res}{space 2}-.0696985{col 28}{space 2} .2007858{col 39}{space 1}   -0.35{col 48}{space 3}0.729{col 56}{space 4}-.4634801{col 69}{space 3} .3240831
{txt}{space 12}3  {c |}{col 16}{res}{space 2}-.2687688{col 28}{space 2} .7383103{col 39}{space 1}   -0.36{col 48}{space 3}0.716{col 56}{space 4}-1.716745{col 69}{space 3} 1.179208
{txt}{space 12}4  {c |}{col 16}{res}{space 2}        3{col 28}{space 2} 2.661769{col 39}{space 1}    1.13{col 48}{space 3}0.260{col 56}{space 4}-2.220269{col 69}{space 3} 8.220269
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:addrvictimindex}{p_end}
{res}{txt}
{com}. graph save g8
{res}{txt}file {bf:g8.gph} saved

{com}. graph combine "g7.gph" "g8.gph"
{res}{txt}
{com}. 
. *Moderated Effects of Victimization Experience on Social Distance (Interflex Estimates)
. 
. interflex magukbias victimorder addrvictimindex, vce(robust)
{txt}p value of Wald test: {res}0.4777
{txt}
{com}. graph save g9
{res}{txt}file {bf:g9.gph} saved

{com}. interflex magcitizenbias victimorder addrvictimindex, vce(robust)
{txt}p value of Wald test: {res}0.1091
{txt}
{com}. graph save g10
{res}{txt}file {bf:g10.gph} saved

{com}. graph combine "g9.gph" "g10.gph"
{res}{txt}
{com}. 
. *Balance Tests on Victimization Treatment 
. ksmirnov rinjured, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0018       0.997
{txt}Combined K-S       {res}  0.0018       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov rfaminjured, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0575       0.037
{txt}Combined K-S       {res}  0.0575       0.074

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov rsexassault, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0058       0.967
{txt}Combined K-S       {res}  0.0058       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov rhomedestroyed, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0220       0.616
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0220       0.969

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov moved, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0120       0.866
{txt}Combined K-S       {res}  0.0120       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov occupied, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0052       0.973
{txt}Combined K-S       {res}  0.0052       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov russian, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0025       0.994
{txt}Combined K-S       {res}  0.0025       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov russpeaker, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0127       0.852
{txt}Combined K-S       {res}  0.0127       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov surveylang, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0096       0.912
{txt}Combined K-S       {res}  0.0096       1.000

{txt}Note: Ties exist in combined dataset;
      there are 6 unique values out of 2000 observations.

{com}. ksmirnov female, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0388       0.221
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0388       0.438

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. ksmirnov age, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0303       0.399
{txt}1                  {res} -0.0330       0.337
{txt}Combined K-S       {res}  0.0330       0.648

{txt}Note: Ties exist in combined dataset;
      there are 72 unique values out of 2000 observations.

{com}. ksmirnov education, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0082       0.935
{txt}1                  {res} -0.0189       0.701
{txt}Combined K-S       {res}  0.0189       0.994

{txt}Note: Ties exist in combined dataset;
      there are 8 unique values out of 1995 observations.

{com}. ksmirnov income, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0075       0.946
{txt}1                  {res} -0.0236       0.580
{txt}Combined K-S       {res}  0.0236       0.948

{txt}Note: Ties exist in combined dataset;
      there are 7 unique values out of 1959 observations.

{com}. ksmirnov region4, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0079       0.939
{txt}1                  {res} -0.0060       0.964
{txt}Combined K-S       {res}  0.0079       1.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 2000 observations.

{com}. ksmirnov rural, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0344       0.307
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0344       0.597

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 2000 observations.

{com}. 
. *note: see also 
. iebaltab rinjured rfaminjured rsexassault moved occupied female age education russian russpeaker occupation income rural region4, groupvar(victimorder) savexlsx(victimbalance)

{res}{phang}Balance table saved in Excel format to: {browse "victimbalance.xlsx":victimbalance.xlsx}{p_end}
{txt}
{com}. 
. *Victimization Treatment Effects Sensitivity Analysis
. 
. reg magukbias victimorder##c.addrvictimindex, robust

{txt}Linear regression                               Number of obs     = {res}     1,832
                                                {txt}F(3, 1828)        =  {res}     2.29
                                                {txt}Prob > F          = {res}    0.0760
                                                {txt}R-squared         = {res}    0.0038
                                                {txt}Root MSE          =    {res} 3.8713

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}        magukbias{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.victimorder {c |}{col 19}{res}{space 2} -.454768{col 31}{space 2} .2280758{col 42}{space 1}   -1.99{col 51}{space 3}0.046{col 59}{space 4}-.9020845{col 72}{space 3}-.0074515
{txt}{space 2}addrvictimindex {c |}{col 19}{res}{space 2}  .088306{col 31}{space 2} .2163663{col 42}{space 1}    0.41{col 51}{space 3}0.683{col 59}{space 4}-.3360451{col 72}{space 3} .5126572
{txt}{space 17} {c |}
{space 6}victimorder#{c |}
c.addrvictimindex {c |}
{space 15}1  {c |}{col 19}{res}{space 2} .3073621{col 31}{space 2}  .305871{col 42}{space 1}    1.00{col 51}{space 3}0.315{col 59}{space 4}-.2925311{col 72}{space 3} .9072554
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} 5.134826{col 31}{space 2} .1658482{col 42}{space 1}   30.96{col 51}{space 3}0.000{col 59}{space 4} 4.809555{col 72}{space 3} 5.460098
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg magukbias victimorder##c.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,800
                                                {txt}F(28, 1771)       =  {res}     7.97
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0909
                                                {txt}Root MSE          =    {res}   3.73

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  magukbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.5139835{col 41}{space 2} .2247459{col 52}{space 1}   -2.29{col 61}{space 3}0.022{col 69}{space 4}-.9547786{col 82}{space 3}-.0731885
{txt}{space 12}addrvictimindex {c |}{col 29}{res}{space 2}-.3291565{col 41}{space 2} .3495727{col 52}{space 1}   -0.94{col 61}{space 3}0.347{col 69}{space 4}-1.014775{col 82}{space 3}  .356462
{txt}{space 27} {c |}
{space 16}victimorder#{c |}
{space 10}c.addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2}  .344544{col 41}{space 2} .3094566{col 52}{space 1}    1.11{col 61}{space 3}0.266{col 69}{space 4}-.2623945{col 82}{space 3} .9514826
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.2536484{col 41}{space 2} .1775974{col 52}{space 1}   -1.43{col 61}{space 3}0.153{col 69}{space 4}-.6019709{col 82}{space 3} .0946742
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2}-.1658737{col 41}{space 2} .9360499{col 52}{space 1}   -0.18{col 61}{space 3}0.859{col 69}{space 4}-2.001753{col 82}{space 3} 1.670005
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .3843843{col 41}{space 2}  .350319{col 52}{space 1}    1.10{col 61}{space 3}0.273{col 69}{space 4} -.302698{col 82}{space 3} 1.071467
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2} .2580429{col 41}{space 2}  .782793{col 52}{space 1}    0.33{col 61}{space 3}0.742{col 69}{space 4}-1.277253{col 82}{space 3} 1.793338
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .1924009{col 41}{space 2} .2898481{col 52}{space 1}    0.66{col 61}{space 3}0.507{col 69}{space 4}-.3760795{col 82}{space 3} .7608813
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .5889504{col 41}{space 2} .4886248{col 52}{space 1}    1.21{col 61}{space 3}0.228{col 69}{space 4}-.3693916{col 82}{space 3} 1.547292
{txt}{space 21}female {c |}{col 29}{res}{space 2} .5268387{col 41}{space 2} .1871091{col 52}{space 1}    2.82{col 61}{space 3}0.005{col 69}{space 4} .1598608{col 82}{space 3} .8938167
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0347146{col 41}{space 2} .0081037{col 52}{space 1}   -4.28{col 61}{space 3}0.000{col 69}{space 4}-.0506085{col 82}{space 3}-.0188207
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0467214{col 41}{space 2} .0642586{col 52}{space 1}   -0.73{col 61}{space 3}0.467{col 69}{space 4} -.172752{col 82}{space 3} .0793092
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-2.222608{col 41}{space 2} .5279473{col 52}{space 1}   -4.21{col 61}{space 3}0.000{col 69}{space 4}-3.258073{col 82}{space 3}-1.187143
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-1.017516{col 41}{space 2} .2626996{col 52}{space 1}   -3.87{col 61}{space 3}0.000{col 69}{space 4}-1.532749{col 82}{space 3}-.5022818
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} -.111272{col 41}{space 2} .3841832{col 52}{space 1}   -0.29{col 61}{space 3}0.772{col 69}{space 4}-.8647722{col 82}{space 3} .6422281
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .1335383{col 41}{space 2} .3168857{col 52}{space 1}    0.42{col 61}{space 3}0.674{col 69}{space 4}-.4879711{col 82}{space 3} .7550477
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2}-.9481921{col 41}{space 2} .4345737{col 52}{space 1}   -2.18{col 61}{space 3}0.029{col 69}{space 4}-1.800523{col 82}{space 3}-.0958608
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2}-.1606969{col 41}{space 2} .4275316{col 52}{space 1}   -0.38{col 61}{space 3}0.707{col 69}{space 4}-.9992165{col 82}{space 3} .6778227
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} -.102412{col 41}{space 2} .5666669{col 52}{space 1}   -0.18{col 61}{space 3}0.857{col 69}{space 4}-1.213818{col 82}{space 3} 1.008994
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0154685{col 41}{space 2} .4287643{col 52}{space 1}   -0.04{col 61}{space 3}0.971{col 69}{space 4}-.8564059{col 82}{space 3} .8254689
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .2840183{col 41}{space 2} .3507585{col 52}{space 1}    0.81{col 61}{space 3}0.418{col 69}{space 4} -.403926{col 82}{space 3} .9719625
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-1.244997{col 41}{space 2} .5117195{col 52}{space 1}   -2.43{col 61}{space 3}0.015{col 69}{space 4}-2.248635{col 82}{space 3}-.2413594
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.8572101{col 41}{space 2} .4046814{col 52}{space 1}   -2.12{col 61}{space 3}0.034{col 69}{space 4}-1.650914{col 82}{space 3}-.0635066
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .1209071{col 41}{space 2} .0851772{col 52}{space 1}    1.42{col 61}{space 3}0.156{col 69}{space 4}-.0461514{col 82}{space 3} .2879656
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .2510599{col 41}{space 2} .2310832{col 52}{space 1}    1.09{col 61}{space 3}0.277{col 69}{space 4}-.2021646{col 82}{space 3} .7042845
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.8125459{col 41}{space 2} .2324829{col 52}{space 1}   -3.50{col 61}{space 3}0.000{col 69}{space 4}-1.268516{col 82}{space 3}-.3565762
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-1.048276{col 41}{space 2} .2790894{col 52}{space 1}   -3.76{col 61}{space 3}0.000{col 69}{space 4}-1.595655{col 82}{space 3}-.5008968
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.811576{col 41}{space 2} .3610453{col 52}{space 1}   -5.02{col 61}{space 3}0.000{col 69}{space 4}-2.519696{col 82}{space 3}-1.103457
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 7.678015{col 41}{space 2} .8044666{col 52}{space 1}    9.54{col 61}{space 3}0.000{col 69}{space 4} 6.100211{col 82}{space 3} 9.255819
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg magcitizenbias victimorder##c.addrvictimindex, robust

{txt}Linear regression                               Number of obs     = {res}     1,924
                                                {txt}F(3, 1920)        =  {res}     1.78
                                                {txt}Prob > F          = {res}    0.1497
                                                {txt}R-squared         = {res}    0.0030
                                                {txt}Root MSE          =    {res} 2.7325

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}   magcitizenbias{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.victimorder {c |}{col 19}{res}{space 2} -.326847{col 31}{space 2}  .157484{col 42}{space 1}   -2.08{col 51}{space 3}0.038{col 59}{space 4}-.6357047{col 72}{space 3}-.0179893
{txt}{space 2}addrvictimindex {c |}{col 19}{res}{space 2}-.0016632{col 31}{space 2} .1645041{col 42}{space 1}   -0.01{col 51}{space 3}0.992{col 59}{space 4}-.3242887{col 72}{space 3} .3209624
{txt}{space 17} {c |}
{space 6}victimorder#{c |}
c.addrvictimindex {c |}
{space 15}1  {c |}{col 19}{res}{space 2} .2189484{col 31}{space 2} .2281525{col 42}{space 1}    0.96{col 51}{space 3}0.337{col 59}{space 4}-.2285045{col 72}{space 3} .6664012
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} 8.453102{col 31}{space 2} .1074567{col 42}{space 1}   78.67{col 51}{space 3}0.000{col 59}{space 4} 8.242358{col 72}{space 3} 8.663846
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg magcitizenbias victimorder##c.addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust

{txt}Linear regression                               Number of obs     = {res}     1,883
                                                {txt}F(28, 1854)       =  {res}     4.22
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0772
                                                {txt}Root MSE          =    {res} 2.6492

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             magcitizenbias{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.victimorder {c |}{col 29}{res}{space 2}-.3817418{col 41}{space 2} .1551378{col 52}{space 1}   -2.46{col 61}{space 3}0.014{col 69}{space 4}-.6860049{col 82}{space 3}-.0774787
{txt}{space 12}addrvictimindex {c |}{col 29}{res}{space 2}-.2933996{col 41}{space 2} .2861444{col 52}{space 1}   -1.03{col 61}{space 3}0.305{col 69}{space 4}-.8545987{col 82}{space 3} .2677996
{txt}{space 27} {c |}
{space 16}victimorder#{c |}
{space 10}c.addrvictimindex {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .2414888{col 41}{space 2} .2340489{col 52}{space 1}    1.03{col 61}{space 3}0.302{col 69}{space 4}-.2175382{col 82}{space 3} .7005158
{txt}{space 27} {c |}
{space 16}ethnicorder {c |}{col 29}{res}{space 2}-.0791168{col 41}{space 2} .1221215{col 52}{space 1}   -0.65{col 61}{space 3}0.517{col 69}{space 4}-.3186268{col 82}{space 3} .1603932
{txt}{space 17}1.rinjured {c |}{col 29}{res}{space 2} .4731684{col 41}{space 2} .7273582{col 52}{space 1}    0.65{col 61}{space 3}0.515{col 69}{space 4}-.9533588{col 82}{space 3} 1.899696
{txt}{space 14}1.rfaminjured {c |}{col 29}{res}{space 2} .2639131{col 41}{space 2} .2610925{col 52}{space 1}    1.01{col 61}{space 3}0.312{col 69}{space 4}-.2481531{col 82}{space 3} .7759794
{txt}{space 14}1.rsexassault {c |}{col 29}{res}{space 2}  .063017{col 41}{space 2} .8567611{col 52}{space 1}    0.07{col 61}{space 3}0.941{col 69}{space 4}-1.617301{col 82}{space 3} 1.743335
{txt}{space 22}moved {c |}{col 29}{res}{space 2} .4549538{col 41}{space 2} .1969918{col 52}{space 1}    2.31{col 61}{space 3}0.021{col 69}{space 4} .0686048{col 82}{space 3} .8413028
{txt}{space 19}occupied {c |}{col 29}{res}{space 2} .5248018{col 41}{space 2} .3437038{col 52}{space 1}    1.53{col 61}{space 3}0.127{col 69}{space 4}-.1492855{col 82}{space 3} 1.198889
{txt}{space 21}female {c |}{col 29}{res}{space 2} .3603791{col 41}{space 2} .1401639{col 52}{space 1}    2.57{col 61}{space 3}0.010{col 69}{space 4} .0854835{col 82}{space 3} .6352747
{txt}{space 24}age {c |}{col 29}{res}{space 2} .0001514{col 41}{space 2} .0054364{col 52}{space 1}    0.03{col 61}{space 3}0.978{col 69}{space 4}-.0105107{col 82}{space 3} .0108134
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.0320187{col 41}{space 2} .0451276{col 52}{space 1}   -0.71{col 61}{space 3}0.478{col 69}{space 4}-.1205249{col 82}{space 3} .0564875
{txt}{space 20}russian {c |}{col 29}{res}{space 2} -1.68252{col 41}{space 2} .5453788{col 52}{space 1}   -3.09{col 61}{space 3}0.002{col 69}{space 4}-2.752141{col 82}{space 3}-.6128995
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-.9283514{col 41}{space 2} .2056766{col 52}{space 1}   -4.51{col 61}{space 3}0.000{col 69}{space 4}-1.331733{col 82}{space 3}-.5249694
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .0891802{col 41}{space 2} .2699548{col 52}{space 1}    0.33{col 61}{space 3}0.741{col 69}{space 4}-.4402671{col 82}{space 3} .6186275
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .0516484{col 41}{space 2} .2351451{col 52}{space 1}    0.22{col 61}{space 3}0.826{col 69}{space 4}-.4095287{col 82}{space 3} .5128254
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .0953737{col 41}{space 2} .3017433{col 52}{space 1}    0.32{col 61}{space 3}0.752{col 69}{space 4}-.4964186{col 82}{space 3}  .687166
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .2385688{col 41}{space 2} .2978126{col 52}{space 1}    0.80{col 61}{space 3}0.423{col 69}{space 4}-.3455146{col 82}{space 3} .8226521
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .7936211{col 41}{space 2} .3487294{col 52}{space 1}    2.28{col 61}{space 3}0.023{col 69}{space 4} .1096777{col 82}{space 3} 1.477565
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2}-.0833139{col 41}{space 2}  .315588{col 52}{space 1}   -0.26{col 61}{space 3}0.792{col 69}{space 4}-.7022589{col 82}{space 3} .5356312
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .1435771{col 41}{space 2} .2457987{col 52}{space 1}    0.58{col 61}{space 3}0.559{col 69}{space 4}-.3384942{col 82}{space 3} .6256483
{txt}{space 19}Student  {c |}{col 29}{res}{space 2} .0720239{col 41}{space 2}  .370912{col 52}{space 1}    0.19{col 61}{space 3}0.846{col 69}{space 4}-.6554251{col 82}{space 3} .7994728
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.4089573{col 41}{space 2} .3077993{col 52}{space 1}   -1.33{col 61}{space 3}0.184{col 69}{space 4}-1.012627{col 82}{space 3} .1947123
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} .0897034{col 41}{space 2} .0607802{col 52}{space 1}    1.48{col 61}{space 3}0.140{col 69}{space 4}-.0295014{col 82}{space 3} .2089082
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2}  .206647{col 41}{space 2} .1522301{col 52}{space 1}    1.36{col 61}{space 3}0.175{col 69}{space 4}-.0919133{col 82}{space 3} .5052074
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.0692258{col 41}{space 2} .1588913{col 52}{space 1}   -0.44{col 61}{space 3}0.663{col 69}{space 4}-.3808504{col 82}{space 3} .2423989
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-.1930616{col 41}{space 2} .1974793{col 52}{space 1}   -0.98{col 61}{space 3}0.328{col 69}{space 4}-.5803668{col 82}{space 3} .1942436
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.064271{col 41}{space 2} .2642194{col 52}{space 1}   -4.03{col 61}{space 3}0.000{col 69}{space 4} -1.58247{col 82}{space 3}-.5460719
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 8.387859{col 41}{space 2} .5522442{col 52}{space 1}   15.19{col 61}{space 3}0.000{col 69}{space 4} 7.304773{col 82}{space 3} 9.470945
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. regsensitivity bounds magukbias victimorder addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust plot
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,800
{col 48}{txt}Beta(short){col 67}{res}=      -0.360
{txt}Treatment{col 18}{res}: victimorder{col 48}{txt}Beta(medium){col 67}{res}=      -0.375
{txt}Outcome{col 18}{res}: magukbias{col 48}{txt}R2(short){col 67}{res}=       0.002
{col 48}{txt}R2(medium){col 67}{res}=       0.090
{col 48}{txt}Var(Y){col 67}{res}=      15.066
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.246

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        35.1%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.3748{txt}, {res}-0.3748{txt} ]
{col 2}{res}0.101{col 35}{txt}[{res}-0.4761{txt}, {res}-0.2736{txt} ]
{col 2}{res}0.202{col 35}{txt}[{res}-0.5807{txt}, {res}-0.1690{txt} ]
{col 2}{res}0.302{col 35}{txt}[{res}-0.6922{txt}, {res}-0.0575{txt} ]
{col 2}{res}0.403{col 35}{txt}[{res}-0.8160{txt}, {res} 0.0663{txt} ]
{col 2}{res}0.504{col 35}{txt}[{res}-0.9599{txt}, {res} 0.2102{txt} ]
{col 2}{res}0.605{col 35}{txt}[{res}-1.1380{txt}, {res} 0.3883{txt} ]
{col 2}{res}0.706{col 35}{txt}[{res}-1.3794{txt}, {res} 0.6297{txt} ]
{col 2}{res}0.807{col 35}{txt}[{res}-1.7619{txt}, {res} 1.0122{txt} ]
{col 2}{res}0.907{col 35}{txt}[{res}-2.6293{txt}, {res} 1.8796{txt} ]
{col 2}{res}0.991{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}
{res}{txt}
{com}. regsensitivity bounds magcitizenbias victimorder addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, robust plot
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=       1,883
{col 48}{txt}Beta(short){col 67}{res}=      -0.266
{txt}Treatment{col 18}{res}: victimorder{col 48}{txt}Beta(medium){col 67}{res}=      -0.284
{txt}Outcome{col 18}{res}: magcitizenbias{col 48}{txt}R2(short){col 67}{res}=       0.002
{col 48}{txt}R2(medium){col 67}{res}=       0.077
{col 48}{txt}Var(Y){col 67}{res}=       7.492
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.247

{txt}Hypothesis{col 18}{res}: Beta < 0         {col 48}{txt}Breakdown point{col 67}{res}=        40.8%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res}-0.2841{txt}, {res}-0.2841{txt} ]
{col 2}{res}0.101{col 35}{txt}[{res}-0.3486{txt}, {res}-0.2195{txt} ]
{col 2}{res}0.203{col 35}{txt}[{res}-0.4152{txt}, {res}-0.1529{txt} ]
{col 2}{res}0.304{col 35}{txt}[{res}-0.4864{txt}, {res}-0.0817{txt} ]
{col 2}{res}0.405{col 35}{txt}[{res}-0.5653{txt}, {res}-0.0028{txt} ]
{col 2}{res}0.506{col 35}{txt}[{res}-0.6573{txt}, {res} 0.0892{txt} ]
{col 2}{res}0.608{col 35}{txt}[{res}-0.7712{txt}, {res} 0.2031{txt} ]
{col 2}{res}0.709{col 35}{txt}[{res}-0.9260{txt}, {res} 0.3579{txt} ]
{col 2}{res}0.810{col 35}{txt}[{res}-1.1726{txt}, {res} 0.6045{txt} ]
{col 2}{res}0.912{col 35}{txt}[{res}-1.7413{txt}, {res} 1.1732{txt} ]
{col 2}{res}0.993{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}
{res}{txt}
{com}. regsensitivity bounds magukbias victimorder addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, oster robust plot
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,800
{col 48}{txt}Beta(short){col 67}{res}=      -0.360
{txt}Treatment{col 18}{res}: victimorder{col 48}{txt}Beta(medium){col 67}{res}=      -0.375
{txt}Outcome{col 18}{res}: magukbias{col 48}{txt}R2(short){col 67}{res}=       0.002
{col 48}{txt}R2(medium){col 67}{res}=       0.090
{col 48}{txt}Var(Y){col 67}{res}=      15.066
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.246

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=         430%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}-0.2441{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}-0.2660{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}-0.2905{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}-0.3167{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}-0.3447{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}-0.3748{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}-0.4073{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}-0.4422{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}-0.4801{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res}-0.5213{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-0.5638{txt}, {res}      .{txt}, {res}      .{txt} }
{hline 80}
{res}{txt}
{com}. regsensitivity bounds magcitizenbias victimorder addrvictimindex ethnicorder i.rinjured i.rfaminjured i.rsexassault moved occupied female age education russian russpeaker i.occupation income urban_rural i.region4, oster robust plot
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=       1,883
{col 48}{txt}Beta(short){col 67}{res}=      -0.266
{txt}Treatment{col 18}{res}: victimorder{col 48}{txt}Beta(medium){col 67}{res}=      -0.284
{txt}Outcome{col 18}{res}: magcitizenbias{col 48}{txt}R2(short){col 67}{res}=       0.002
{col 48}{txt}R2(medium){col 67}{res}=       0.077
{col 48}{txt}Var(Y){col 67}{res}=       7.492
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.247

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=         162%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}-0.0941{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}-0.1261{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}-0.1619{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}-0.2000{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}-0.2406{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}-0.2841{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}-0.3307{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res}-0.3808{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.600{col 35}{txt}{{res}-0.4349{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.800{col 35}{txt}{{res}-0.4934{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-0.5537{txt}, {res}      .{txt}, {res}      .{txt} }
{hline 80}
{res}{txt}
{com}. 
. *Intercorrelation among Victimization Items 
. 
. pwcorr rinjured rfaminjured rsexassault rhomedestroyed moved occupied, sig

             {txt}{c |} rinjured rfamin~d rsexas~t rhomed~d    moved occupied
{hline 13}{c +}{hline 54}
    rinjured {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
 rfaminjured {c |} {res} -0.0023   1.0000 
             {txt}{c |}{res}   0.9197
             {txt}{c |}
 rsexassault {c |} {res} -0.0089   0.1095   1.0000 
             {txt}{c |}{res}   0.6923   0.0000
             {txt}{c |}
rhomedestr~d {c |} {res}  0.0568   0.0371   0.0666   1.0000 
             {txt}{c |}{res}   0.0110   0.0971   0.0029
             {txt}{c |}
       moved {c |} {res} -0.0161   0.0287  -0.0003   0.2169   1.0000 
             {txt}{c |}{res}   0.4715   0.1991   0.9876   0.0000
             {txt}{c |}
    occupied {c |} {res}  0.0153   0.0344  -0.0207   0.1774   0.4602   1.0000 
             {txt}{c |}{res}   0.4937   0.1236   0.3555   0.0000   0.0000
             {txt}{c |}

{com}. 
. *Factor Analysis of Victimization-Related Items
. 
. factor rinjured rfaminjured rsexassault rhomedestroyed moved occupied
{txt}(obs=2,000)

Factor analysis/correlation{col 50}Number of obs    = {res}     2,000
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      15

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.79741      0.65354            1.4484       1.4484
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.14387      0.10943            0.2613       1.7098
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.03444      0.09403            0.0626       1.7723
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.05958      0.06426           -0.1082       1.6641
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.12385      0.11791           -0.2250       1.4391
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.24176            .           -0.4391       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}15{txt}) ={res}  636.08{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:rinjured}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0217}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0225}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1602}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9734}}}{space 1}
{space 4}{space 0}{ralign 12:rfaminjured}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0654}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2330}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0399}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9398}}}{space 1}
{space 4}{space 0}{ralign 12:rsexassault}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0209}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2680}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0180}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9274}}}{space 1}
{space 4}{space 0}{ralign 12:rhomedestr~d}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3225}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1108}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0768}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8778}}}{space 1}
{space 4}{space 0}{ralign 12:moved}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5959}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0393}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0259}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6427}}}{space 1}
{space 4}{space 0}{ralign 12:occupied}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5772}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0583}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0170}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6631}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *Correlates of Victimization (Logit Regression)
. 
. logit rinjured  female age education russian russpeaker i.occupation income urban_rural i.region4 date, cluster(oblast)

{txt}note: {bf:russian} != 0 predicts failure perfectly;
      {bf:russian} omitted and 48 obs not used.

note: {bf:2.occupation} != 0 predicts failure perfectly;
      {bf:2.occupation} omitted and 162 obs not used.

note: {bf:3.occupation} != 0 predicts failure perfectly;
      {bf:3.occupation} omitted and 402 obs not used.

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-68.541319}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-64.778174}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-60.095258}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-59.981162}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-59.980998}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-59.980998}  
{res}
{txt}{col 1}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:1,341}
{txt}{col 56}{lalign 13:Wald chi2({res:17})}{col 69} = {res}{ralign 7:4289.16}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-59.980998}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.1249}

{txt}{ralign 93:(Std. err. adjusted for {res:25} clusters in {res:oblast})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                   rinjured{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}female {c |}{col 29}{res}{space 2}-.4902633{col 41}{space 2} .6971199{col 52}{space 1}   -0.70{col 61}{space 3}0.482{col 69}{space 4}-1.856593{col 82}{space 3} .8760666
{txt}{space 24}age {c |}{col 29}{res}{space 2} .0222362{col 41}{space 2} .0181921{col 52}{space 1}    1.22{col 61}{space 3}0.222{col 69}{space 4}-.0134197{col 82}{space 3} .0578922
{txt}{space 18}education {c |}{col 29}{res}{space 2} .1397334{col 41}{space 2} .1314228{col 52}{space 1}    1.06{col 61}{space 3}0.288{col 69}{space 4}-.1178506{col 82}{space 3} .3973173
{txt}{space 20}russian {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 17}russpeaker {c |}{col 29}{res}{space 2} .3288814{col 41}{space 2} .3375913{col 52}{space 1}    0.97{col 61}{space 3}0.330{col 69}{space 4}-.3327855{col 82}{space 3} .9905482
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (empty)
Professional (with high..)  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (empty)
Self employed businesswo..  {c |}{col 29}{res}{space 2} 1.297836{col 41}{space 2} 1.482273{col 52}{space 1}    0.88{col 61}{space 3}0.381{col 69}{space 4}-1.607367{col 82}{space 3} 4.203038
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} 1.546872{col 41}{space 2} 1.303348{col 52}{space 1}    1.19{col 61}{space 3}0.235{col 69}{space 4}-1.007642{col 82}{space 3} 4.101387
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} 3.774755{col 41}{space 2} 1.062163{col 52}{space 1}    3.55{col 61}{space 3}0.000{col 69}{space 4} 1.692953{col 82}{space 3} 5.856557
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2} .9601801{col 41}{space 2}  1.74865{col 52}{space 1}    0.55{col 61}{space 3}0.583{col 69}{space 4}-2.467112{col 82}{space 3} 4.387472
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .0845468{col 41}{space 2} 1.242201{col 52}{space 1}    0.07{col 61}{space 3}0.946{col 69}{space 4}-2.350122{col 82}{space 3} 2.519216
{txt}{space 19}Student  {c |}{col 29}{res}{space 2} 2.613955{col 41}{space 2} 1.785563{col 52}{space 1}    1.46{col 61}{space 3}0.143{col 69}{space 4} -.885683{col 82}{space 3} 6.113594
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}  .510081{col 41}{space 2} 1.398109{col 52}{space 1}    0.36{col 61}{space 3}0.715{col 69}{space 4}-2.230162{col 82}{space 3} 3.250324
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2}-.5501035{col 41}{space 2} .2313052{col 52}{space 1}   -2.38{col 61}{space 3}0.017{col 69}{space 4}-1.003453{col 82}{space 3}-.0967538
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2}-.1689491{col 41}{space 2} .8760629{col 52}{space 1}   -0.19{col 61}{space 3}0.847{col 69}{space 4}-1.886001{col 82}{space 3} 1.548103
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2} .8685993{col 41}{space 2} .9969132{col 52}{space 1}    0.87{col 61}{space 3}0.384{col 69}{space 4}-1.085315{col 82}{space 3} 2.822513
{txt}{space 21}South  {c |}{col 29}{res}{space 2} 1.179518{col 41}{space 2} 1.057511{col 52}{space 1}    1.12{col 61}{space 3}0.265{col 69}{space 4} -.893165{col 82}{space 3} 3.252201
{txt}{space 22}East  {c |}{col 29}{res}{space 2}  .062159{col 41}{space 2}  1.16821{col 52}{space 1}    0.05{col 61}{space 3}0.958{col 69}{space 4}-2.227491{col 82}{space 3} 2.351809
{txt}{space 27} {c |}
{space 23}date {c |}{col 29}{res}{space 2}-.0896856{col 41}{space 2} .0938033{col 52}{space 1}   -0.96{col 61}{space 3}0.339{col 69}{space 4}-.2735366{col 82}{space 3} .0941655
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-5.280125{col 41}{space 2} 1.944087{col 52}{space 1}   -2.72{col 61}{space 3}0.007{col 69}{space 4}-9.090465{col 82}{space 3}-1.469784
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit rfaminjured  female age education russian russpeaker i.occupation income urban_rural i.region4 date, cluster(oblast)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-1187.9764}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -1151.997}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1151.0299}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1151.0225}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1151.0225}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,953}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:521.68}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1151.0225}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0311}

{txt}{ralign 93:(Std. err. adjusted for {res:25} clusters in {res:oblast})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                rfaminjured{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}female {c |}{col 29}{res}{space 2}-.0475989{col 41}{space 2} .1263351{col 52}{space 1}   -0.38{col 61}{space 3}0.706{col 69}{space 4}-.2952111{col 82}{space 3} .2000132
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0148116{col 41}{space 2} .0033971{col 52}{space 1}   -4.36{col 61}{space 3}0.000{col 69}{space 4}-.0214698{col 82}{space 3}-.0081534
{txt}{space 18}education {c |}{col 29}{res}{space 2} .0426012{col 41}{space 2} .0295129{col 52}{space 1}    1.44{col 61}{space 3}0.149{col 69}{space 4} -.015243{col 82}{space 3} .1004454
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-1.188474{col 41}{space 2} .5365976{col 52}{space 1}   -2.21{col 61}{space 3}0.027{col 69}{space 4}-2.240186{col 82}{space 3}-.1367625
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-.3020887{col 41}{space 2} .2004779{col 52}{space 1}   -1.51{col 61}{space 3}0.132{col 69}{space 4}-.6950181{col 82}{space 3} .0908406
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2}  .000178{col 41}{space 2} .1857296{col 52}{space 1}    0.00{col 61}{space 3}0.999{col 69}{space 4}-.3638453{col 82}{space 3} .3642014
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .0297834{col 41}{space 2} .1701394{col 52}{space 1}    0.18{col 61}{space 3}0.861{col 69}{space 4}-.3036837{col 82}{space 3} .3632504
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .0884541{col 41}{space 2} .3390041{col 52}{space 1}    0.26{col 61}{space 3}0.794{col 69}{space 4}-.5759816{col 82}{space 3} .7528899
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .2646177{col 41}{space 2} .1960757{col 52}{space 1}    1.35{col 61}{space 3}0.177{col 69}{space 4}-.1196835{col 82}{space 3}  .648919
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .6223576{col 41}{space 2} .2932547{col 52}{space 1}    2.12{col 61}{space 3}0.034{col 69}{space 4} .0475889{col 82}{space 3} 1.197126
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2} .0998283{col 41}{space 2} .2295816{col 52}{space 1}    0.43{col 61}{space 3}0.664{col 69}{space 4}-.3501434{col 82}{space 3} .5498001
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2}-.0330914{col 41}{space 2} .1686792{col 52}{space 1}   -0.20{col 61}{space 3}0.844{col 69}{space 4}-.3636966{col 82}{space 3} .2975138
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-1.056806{col 41}{space 2} .3660057{col 52}{space 1}   -2.89{col 61}{space 3}0.004{col 69}{space 4}-1.774164{col 82}{space 3}-.3394477
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.1549124{col 41}{space 2} .1335888{col 52}{space 1}   -1.16{col 61}{space 3}0.246{col 69}{space 4}-.4167417{col 82}{space 3} .1069169
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2}-.0033828{col 41}{space 2} .0488835{col 52}{space 1}   -0.07{col 61}{space 3}0.945{col 69}{space 4}-.0991927{col 82}{space 3} .0924271
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .1682723{col 41}{space 2} .1652948{col 52}{space 1}    1.02{col 61}{space 3}0.309{col 69}{space 4}-.1556996{col 82}{space 3} .4922442
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.1661757{col 41}{space 2} .1569369{col 52}{space 1}   -1.06{col 61}{space 3}0.290{col 69}{space 4}-.4737664{col 82}{space 3}  .141415
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-.2266566{col 41}{space 2} .1376027{col 52}{space 1}   -1.65{col 61}{space 3}0.100{col 69}{space 4}-.4963529{col 82}{space 3} .0430397
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-.3640478{col 41}{space 2} .1848792{col 52}{space 1}   -1.97{col 61}{space 3}0.049{col 69}{space 4}-.7264044{col 82}{space 3}-.0016911
{txt}{space 27} {c |}
{space 23}date {c |}{col 29}{res}{space 2} .0312641{col 41}{space 2} .0165719{col 52}{space 1}    1.89{col 61}{space 3}0.059{col 69}{space 4}-.0012163{col 82}{space 3} .0637445
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-.7625337{col 41}{space 2} .3041733{col 52}{space 1}   -2.51{col 61}{space 3}0.012{col 69}{space 4}-1.358702{col 82}{space 3} -.166365
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit rsexassault  female age education russian russpeaker i.occupation income urban_rural i.region4 date, cluster(oblast)

{txt}note: {bf:russian} != 0 predicts failure perfectly;
      {bf:russian} omitted and 48 obs not used.

note: {bf:5.occupation} != 0 predicts failure perfectly;
      {bf:5.occupation} omitted and 100 obs not used.

note: {bf:6.occupation} != 0 predicts failure perfectly;
      {bf:6.occupation} omitted and 45 obs not used.

note: {bf:9.occupation} != 0 predicts failure perfectly;
      {bf:9.occupation} omitted and 60 obs not used.

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-108.73491}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-103.45831}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-102.52441}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-102.51841}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-102.51839}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-102.51839}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,700}
{txt}{col 57}{lalign 13:Wald chi2({res:16})}{col 70} = {res}{ralign 6:67.05}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-102.51839}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0572}

{txt}{ralign 93:(Std. err. adjusted for {res:25} clusters in {res:oblast})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                rsexassault{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}female {c |}{col 29}{res}{space 2}-.0924387{col 41}{space 2} .5113468{col 52}{space 1}   -0.18{col 61}{space 3}0.857{col 69}{space 4} -1.09466{col 82}{space 3} .9097826
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0328389{col 41}{space 2} .0172122{col 52}{space 1}   -1.91{col 61}{space 3}0.056{col 69}{space 4}-.0665742{col 82}{space 3} .0008963
{txt}{space 18}education {c |}{col 29}{res}{space 2}-.1492826{col 41}{space 2} .1282003{col 52}{space 1}   -1.16{col 61}{space 3}0.244{col 69}{space 4}-.4005507{col 82}{space 3} .1019854
{txt}{space 20}russian {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 17}russpeaker {c |}{col 29}{res}{space 2} .6767595{col 41}{space 2} .7062736{col 52}{space 1}    0.96{col 61}{space 3}0.338{col 69}{space 4}-.7075113{col 82}{space 3}  2.06103
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .9417533{col 41}{space 2} 1.133509{col 52}{space 1}    0.83{col 61}{space 3}0.406{col 69}{space 4}-1.279884{col 82}{space 3} 3.163391
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} 1.414815{col 41}{space 2} .9135582{col 52}{space 1}    1.55{col 61}{space 3}0.121{col 69}{space 4}-.3757264{col 82}{space 3} 3.205356
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .6595836{col 41}{space 2} 1.389637{col 52}{space 1}    0.47{col 61}{space 3}0.635{col 69}{space 4}-2.064054{col 82}{space 3} 3.383222
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (empty)
{space 10}Military servant  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (empty)
{space 15}Householder  {c |}{col 29}{res}{space 2} .1313931{col 41}{space 2} 1.221632{col 52}{space 1}    0.11{col 61}{space 3}0.914{col 69}{space 4}-2.262962{col 82}{space 3} 2.525749
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} 1.162605{col 41}{space 2} .6223549{col 52}{space 1}    1.87{col 61}{space 3}0.062{col 69}{space 4}-.0571884{col 82}{space 3} 2.382398
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (empty)
{space 16}Unemployed  {c |}{col 29}{res}{space 2} .6437564{col 41}{space 2} 1.099585{col 52}{space 1}    0.59{col 61}{space 3}0.558{col 69}{space 4} -1.51139{col 82}{space 3} 2.798903
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2}-.3423918{col 41}{space 2}  .187033{col 52}{space 1}   -1.83{col 61}{space 3}0.067{col 69}{space 4}-.7089698{col 82}{space 3} .0241861
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2}-.4273563{col 41}{space 2}  .804299{col 52}{space 1}   -0.53{col 61}{space 3}0.595{col 69}{space 4}-2.003753{col 82}{space 3} 1.149041
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2}-.3095158{col 41}{space 2} .4501769{col 52}{space 1}   -0.69{col 61}{space 3}0.492{col 69}{space 4}-1.191846{col 82}{space 3} .5728147
{txt}{space 21}South  {c |}{col 29}{res}{space 2}-.9525376{col 41}{space 2} .5705624{col 52}{space 1}   -1.67{col 61}{space 3}0.095{col 69}{space 4}-2.070819{col 82}{space 3} .1657442
{txt}{space 22}East  {c |}{col 29}{res}{space 2}-1.901648{col 41}{space 2} .8051835{col 52}{space 1}   -2.36{col 61}{space 3}0.018{col 69}{space 4}-3.479779{col 82}{space 3}-.3235177
{txt}{space 27} {c |}
{space 23}date {c |}{col 29}{res}{space 2}-.0206685{col 41}{space 2} .0578679{col 52}{space 1}   -0.36{col 61}{space 3}0.721{col 69}{space 4}-.1340876{col 82}{space 3} .0927506
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-.5600079{col 41}{space 2} 2.469928{col 52}{space 1}   -0.23{col 61}{space 3}0.821{col 69}{space 4}-5.400978{col 82}{space 3} 4.280963
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit rhomedestroyed  female age education russian russpeaker i.occupation income urban_rural i.region4 date, cluster(oblast)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-593.69742}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-552.43711}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-528.43303}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-528.28961}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-528.28947}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-528.28947}  
{res}
{txt}{col 1}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:1,953}
{txt}{col 56}{lalign 13:Wald chi2({res:20})}{col 69} = {res}{ralign 7:1158.86}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-528.28947}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.1102}

{txt}{ralign 93:(Std. err. adjusted for {res:25} clusters in {res:oblast})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}             rhomedestroyed{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}female {c |}{col 29}{res}{space 2}-.1650182{col 41}{space 2} .1250106{col 52}{space 1}   -1.32{col 61}{space 3}0.187{col 69}{space 4}-.4100345{col 82}{space 3} .0799981
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0128318{col 41}{space 2} .0081673{col 52}{space 1}   -1.57{col 61}{space 3}0.116{col 69}{space 4}-.0288395{col 82}{space 3} .0031758
{txt}{space 18}education {c |}{col 29}{res}{space 2} .1168876{col 41}{space 2} .0377925{col 52}{space 1}    3.09{col 61}{space 3}0.002{col 69}{space 4} .0428156{col 82}{space 3} .1909595
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-1.046427{col 41}{space 2} .5061972{col 52}{space 1}   -2.07{col 61}{space 3}0.039{col 69}{space 4}-2.038555{col 82}{space 3}-.0542988
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-.0303229{col 41}{space 2} .1687319{col 52}{space 1}   -0.18{col 61}{space 3}0.857{col 69}{space 4}-.3610313{col 82}{space 3} .3003855
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} 1.008039{col 41}{space 2} .3704976{col 52}{space 1}    2.72{col 61}{space 3}0.007{col 69}{space 4} .2818774{col 82}{space 3} 1.734201
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .5579849{col 41}{space 2} .3242285{col 52}{space 1}    1.72{col 61}{space 3}0.085{col 69}{space 4}-.0774913{col 82}{space 3} 1.193461
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .2979398{col 41}{space 2} .4130444{col 52}{space 1}    0.72{col 61}{space 3}0.471{col 69}{space 4}-.5116123{col 82}{space 3} 1.107492
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .7442593{col 41}{space 2} .5141616{col 52}{space 1}    1.45{col 61}{space 3}0.148{col 69}{space 4}-.2634789{col 82}{space 3} 1.751998
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .7704316{col 41}{space 2} .6656532{col 52}{space 1}    1.16{col 61}{space 3}0.247{col 69}{space 4}-.5342248{col 82}{space 3} 2.075088
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2} .2170693{col 41}{space 2} .6612396{col 52}{space 1}    0.33{col 61}{space 3}0.743{col 69}{space 4}-1.078936{col 82}{space 3} 1.513075
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .8774018{col 41}{space 2}  .384164{col 52}{space 1}    2.28{col 61}{space 3}0.022{col 69}{space 4} .1244542{col 82}{space 3} 1.630349
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-.0281593{col 41}{space 2}  .523947{col 52}{space 1}   -0.05{col 61}{space 3}0.957{col 69}{space 4}-1.055076{col 82}{space 3} .9987579
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.2886218{col 41}{space 2} .3859795{col 52}{space 1}   -0.75{col 61}{space 3}0.455{col 69}{space 4}-1.045128{col 82}{space 3} .4678841
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2}-.0289148{col 41}{space 2} .0550539{col 52}{space 1}   -0.53{col 61}{space 3}0.599{col 69}{space 4}-.1368185{col 82}{space 3} .0789889
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2}-.0707191{col 41}{space 2} .1905635{col 52}{space 1}   -0.37{col 61}{space 3}0.711{col 69}{space 4}-.4442167{col 82}{space 3} .3027784
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2} 1.315144{col 41}{space 2} .4901483{col 52}{space 1}    2.68{col 61}{space 3}0.007{col 69}{space 4}  .354471{col 82}{space 3} 2.275817
{txt}{space 21}South  {c |}{col 29}{res}{space 2} 1.021076{col 41}{space 2} .6557618{col 52}{space 1}    1.56{col 61}{space 3}0.119{col 69}{space 4}-.2641937{col 82}{space 3} 2.306345
{txt}{space 22}East  {c |}{col 29}{res}{space 2}  2.81755{col 41}{space 2} .4648786{col 52}{space 1}    6.06{col 61}{space 3}0.000{col 69}{space 4} 1.906405{col 82}{space 3} 3.728696
{txt}{space 27} {c |}
{space 23}date {c |}{col 29}{res}{space 2} .0056068{col 41}{space 2} .0245966{col 52}{space 1}    0.23{col 61}{space 3}0.820{col 69}{space 4}-.0426016{col 82}{space 3} .0538152
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-4.215684{col 41}{space 2}  .831747{col 52}{space 1}   -5.07{col 61}{space 3}0.000{col 69}{space 4}-5.845878{col 82}{space 3}-2.585489
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit moved  female age education russian russpeaker i.occupation income urban_rural i.region4 date, cluster(oblast)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-788.31168}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-689.84842}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-653.51307}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-652.69297}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-652.68936}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-652.68936}  
{res}
{txt}{col 1}Logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:1,953}
{txt}{col 56}{lalign 13:Wald chi2({res:20})}{col 69} = {res}{ralign 7:1263.07}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-652.68936}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.1720}

{txt}{ralign 93:(Std. err. adjusted for {res:25} clusters in {res:oblast})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      moved{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}female {c |}{col 29}{res}{space 2} .1037815{col 41}{space 2} .1983947{col 52}{space 1}    0.52{col 61}{space 3}0.601{col 69}{space 4}-.2850649{col 82}{space 3} .4926279
{txt}{space 24}age {c |}{col 29}{res}{space 2} -.027723{col 41}{space 2} .0074589{col 52}{space 1}   -3.72{col 61}{space 3}0.000{col 69}{space 4}-.0423421{col 82}{space 3}-.0131039
{txt}{space 18}education {c |}{col 29}{res}{space 2} .0527714{col 41}{space 2} .0502584{col 52}{space 1}    1.05{col 61}{space 3}0.294{col 69}{space 4}-.0457332{col 82}{space 3}  .151276
{txt}{space 20}russian {c |}{col 29}{res}{space 2}-.3601702{col 41}{space 2} .5412189{col 52}{space 1}   -0.67{col 61}{space 3}0.506{col 69}{space 4} -1.42094{col 82}{space 3} .7005993
{txt}{space 17}russpeaker {c |}{col 29}{res}{space 2}-.2169558{col 41}{space 2}  .221331{col 52}{space 1}   -0.98{col 61}{space 3}0.327{col 69}{space 4}-.6507565{col 82}{space 3} .2168449
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .2925164{col 41}{space 2} .2551959{col 52}{space 1}    1.15{col 61}{space 3}0.252{col 69}{space 4}-.2076583{col 82}{space 3} .7926911
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .5305084{col 41}{space 2} .2201076{col 52}{space 1}    2.41{col 61}{space 3}0.016{col 69}{space 4} .0991054{col 82}{space 3} .9619115
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2}-.0026456{col 41}{space 2} .5596734{col 52}{space 1}   -0.00{col 61}{space 3}0.996{col 69}{space 4}-1.099585{col 82}{space 3} 1.094294
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .6545414{col 41}{space 2} .2702245{col 52}{space 1}    2.42{col 61}{space 3}0.015{col 69}{space 4} .1249111{col 82}{space 3} 1.184172
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .7968368{col 41}{space 2} .4906859{col 52}{space 1}    1.62{col 61}{space 3}0.104{col 69}{space 4}-.1648898{col 82}{space 3} 1.758563
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2} .6206586{col 41}{space 2} .2961905{col 52}{space 1}    2.10{col 61}{space 3}0.036{col 69}{space 4}  .040136{col 82}{space 3} 1.201181
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .7223492{col 41}{space 2}  .194655{col 52}{space 1}    3.71{col 61}{space 3}0.000{col 69}{space 4} .3408323{col 82}{space 3} 1.103866
{txt}{space 19}Student  {c |}{col 29}{res}{space 2} .4670753{col 41}{space 2} .5008671{col 52}{space 1}    0.93{col 61}{space 3}0.351{col 69}{space 4}-.5146062{col 82}{space 3} 1.448757
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2} .8035215{col 41}{space 2} .2952259{col 52}{space 1}    2.72{col 61}{space 3}0.006{col 69}{space 4} .2248893{col 82}{space 3} 1.382154
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2}-.0521102{col 41}{space 2}  .048859{col 52}{space 1}   -1.07{col 61}{space 3}0.286{col 69}{space 4}-.1478722{col 82}{space 3} .0436517
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2}  -.04704{col 41}{space 2}  .273469{col 52}{space 1}   -0.17{col 61}{space 3}0.863{col 69}{space 4}-.5830295{col 82}{space 3} .4889494
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 19}Central  {c |}{col 29}{res}{space 2} 1.192926{col 41}{space 2}  .575733{col 52}{space 1}    2.07{col 61}{space 3}0.038{col 69}{space 4} .0645097{col 82}{space 3} 2.321342
{txt}{space 21}South  {c |}{col 29}{res}{space 2} 1.650987{col 41}{space 2} .7192409{col 52}{space 1}    2.30{col 61}{space 3}0.022{col 69}{space 4} .2413006{col 82}{space 3} 3.060673
{txt}{space 22}East  {c |}{col 29}{res}{space 2} 3.429763{col 41}{space 2} .7502846{col 52}{space 1}    4.57{col 61}{space 3}0.000{col 69}{space 4} 1.959232{col 82}{space 3} 4.900293
{txt}{space 27} {c |}
{space 23}date {c |}{col 29}{res}{space 2} .0334484{col 41}{space 2} .0230454{col 52}{space 1}    1.45{col 61}{space 3}0.147{col 69}{space 4}-.0117198{col 82}{space 3} .0786167
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-3.292085{col 41}{space 2} .7473442{col 52}{space 1}   -4.41{col 61}{space 3}0.000{col 69}{space 4}-4.756853{col 82}{space 3}-1.827318
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit occupied  female age education russian russpeaker i.occupation income urban_rural i.region4 date, cluster(oblast)

{txt}note: {bf:russian} != 0 predicts failure perfectly;
      {bf:russian} omitted and 48 obs not used.

note: {bf:1.region4} != 0 predicts failure perfectly;
      {bf:1.region4} omitted and 364 obs not used.

note: {bf:2.region4} != 0 predicts failure perfectly;
      {bf:2.region4} omitted and 826 obs not used.

note: {bf:4.region4} omitted because of collinearity.
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-235.71279}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-222.67328}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-221.06675}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-221.06323}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-221.06323}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:715}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(6)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-221.06323}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0622}

{txt}{ralign 93:(Std. err. adjusted for {res:8} clusters in {res:oblast})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                   occupied{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}female {c |}{col 29}{res}{space 2} .1980554{col 41}{space 2} .2011131{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4} -.196119{col 82}{space 3} .5922297
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0132714{col 41}{space 2} .0110517{col 52}{space 1}   -1.20{col 61}{space 3}0.230{col 69}{space 4}-.0349322{col 82}{space 3} .0083895
{txt}{space 18}education {c |}{col 29}{res}{space 2} .0641908{col 41}{space 2} .1452969{col 52}{space 1}    0.44{col 61}{space 3}0.659{col 69}{space 4}-.2205858{col 82}{space 3} .3489675
{txt}{space 20}russian {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 17}russpeaker {c |}{col 29}{res}{space 2} .0193363{col 41}{space 2} .4137862{col 52}{space 1}    0.05{col 61}{space 3}0.963{col 69}{space 4}-.7916699{col 82}{space 3} .8303424
{txt}{space 27} {c |}
{space 17}occupation {c |}
Servant (without higher..)  {c |}{col 29}{res}{space 2} .1185671{col 41}{space 2} .2718217{col 52}{space 1}    0.44{col 61}{space 3}0.663{col 69}{space 4}-.4141936{col 82}{space 3} .6513278
{txt}Professional (with high..)  {c |}{col 29}{res}{space 2} .1917475{col 41}{space 2} .2421006{col 52}{space 1}    0.79{col 61}{space 3}0.428{col 69}{space 4}-.2827609{col 82}{space 3} .6662558
{txt}Self employed businesswo..  {c |}{col 29}{res}{space 2} .0821981{col 41}{space 2} .8764735{col 52}{space 1}    0.09{col 61}{space 3}0.925{col 69}{space 4}-1.635658{col 82}{space 3} 1.800055
{txt}{space 6}Entrepreneur, farmer  {c |}{col 29}{res}{space 2} .7942557{col 41}{space 2} .5826442{col 52}{space 1}    1.36{col 61}{space 3}0.173{col 69}{space 4}-.3477058{col 82}{space 3} 1.936217
{txt}{space 10}Military servant  {c |}{col 29}{res}{space 2} .1462524{col 41}{space 2} 1.149122{col 52}{space 1}    0.13{col 61}{space 3}0.899{col 69}{space 4}-2.105985{col 82}{space 3}  2.39849
{txt}{space 15}Householder  {c |}{col 29}{res}{space 2} .4965368{col 41}{space 2} .4888836{col 52}{space 1}    1.02{col 61}{space 3}0.310{col 69}{space 4}-.4616575{col 82}{space 3} 1.454731
{txt}Pension (because of age..)  {c |}{col 29}{res}{space 2} .2050075{col 41}{space 2} .4114193{col 52}{space 1}    0.50{col 61}{space 3}0.618{col 69}{space 4}-.6013595{col 82}{space 3} 1.011375
{txt}{space 19}Student  {c |}{col 29}{res}{space 2}-.2623162{col 41}{space 2} .9449686{col 52}{space 1}   -0.28{col 61}{space 3}0.781{col 69}{space 4}-2.114421{col 82}{space 3} 1.589788
{txt}{space 16}Unemployed  {c |}{col 29}{res}{space 2} .9745792{col 41}{space 2} .2128196{col 52}{space 1}    4.58{col 61}{space 3}0.000{col 69}{space 4} .5574605{col 82}{space 3} 1.391698
{txt}{space 27} {c |}
{space 21}income {c |}{col 29}{res}{space 2} -.007861{col 41}{space 2} .1242202{col 52}{space 1}   -0.06{col 61}{space 3}0.950{col 69}{space 4}-.2513282{col 82}{space 3} .2356062
{txt}{space 16}urban_rural {c |}{col 29}{res}{space 2} .5069976{col 41}{space 2} .5913955{col 52}{space 1}    0.86{col 61}{space 3}0.391{col 69}{space 4}-.6521162{col 82}{space 3} 1.666111
{txt}{space 27} {c |}
{space 20}region4 {c |}
{space 22}West  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (empty)
{space 19}Central  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (empty)
{space 21}South  {c |}{col 29}{res}{space 2}-.9540366{col 41}{space 2} 1.197464{col 52}{space 1}   -0.80{col 61}{space 3}0.426{col 69}{space 4}-3.301024{col 82}{space 3} 1.392951
{txt}{space 22}East  {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 27} {c |}
{space 23}date {c |}{col 29}{res}{space 2} .0030205{col 41}{space 2} .0221854{col 52}{space 1}    0.14{col 61}{space 3}0.892{col 69}{space 4} -.040462{col 82}{space 3} .0465031
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-2.484137{col 41}{space 2} 1.498589{col 52}{space 1}   -1.66{col 61}{space 3}0.097{col 69}{space 4}-5.421318{col 82}{space 3} .4530433
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Changes in Social Distance Over Time (July 2022-January 2023)
. 
. *Use "CC Ukraine 2022-2023 combined data.dta"
. 
. *Sample Demographics (Follow-up January 3-11, 2023 survey)
. 
. *Use "CC Ukraine Jan 2023 replication data.dta"
. 
. *Balance Tests (Follow-up January 3-11, 2023 survey Identity Treatments)
. 
. *Use "CC Ukraine Jan 2023 replication data.dta"
. 
. 
. *Power Calculations 
. 
. power oneway, ngroups(2) n1(1011) n2(989) power(0.80 0.90 0.95 0.99)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 8:N_avg}{txt}{txt}{ralign 8:N1}{txt}{txt}{ralign 8:N2}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:2,000}{res}{ralign 8:1000}{res}{ralign 8:1,011}{res}{ralign 8:989}{res}{ralign 8:.06268}{res}{ralign 8:2}{res}{ralign 8:.00393}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:2,000}{res}{ralign 8:1000}{res}{ralign 8:1,011}{res}{ralign 8:989}{res}{ralign 8:.07252}{res}{ralign 8:2}{res}{ralign 8:.00526}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:2,000}{res}{ralign 8:1000}{res}{ralign 8:1,011}{res}{ralign 8:989}{res}{ralign 8:.08064}{res}{ralign 8:2}{res}{ralign 8:.0065}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:2,000}{res}{ralign 8:1000}{res}{ralign 8:1,011}{res}{ralign 8:989}{res}{ralign 8:.09589}{res}{ralign 8:2}{res}{ralign 8:.0092}{res}{ralign 8:1}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. 
. *Effect sizes (Victimization Priming)
. esize twosample magukbias, by(victimorder)

{txt}Effect size based on mean comparison

                               Obs per group:
                              victimorder==0 =        916
                              victimorder==1 =        916
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2} .0879505{col 34}{space 3}-.0036889{col 46}{space 3} .1795659
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .0879145{col 34}{space 3}-.0036874{col 46}{space 3} .1794923
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. esize twosample magcitizenbias, by(victimorder)

{txt}Effect size based on mean comparison

                               Obs per group:
                              victimorder==0 =        966
                              victimorder==1 =        958
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2} .0883898{col 34}{space 3}-.0010328{col 46}{space 3} .1777894
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .0883553{col 34}{space 3}-.0010324{col 46}{space 3}   .17772
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
{res}{txt}
{com}. 
. *Effect sizes (Identity Priming)
. 
. *Use "CC Ukraine Jan 2023 replication data.dta"
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Ukraine War\Ukraine Jan 2023\C&C\CC Revision 1\CC Revision 2\Final Version\CC Replication Instructions\CC Ukraine July 2022 replication log file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}27 Jan 2025, 16:53:28
{txt}{.-}
{smcl}
{txt}{sf}{ul off}